1

H-DenseUNet: Hybrid Densely Connected UNet for
Liver and Tumor Segmentation from CT Volumes
Xiaomeng Li1 , Hao Chen1,2 , Xiaojuan Qi1 , Qi Dou1 , Chi-Wing Fu1 , and Pheng-Ann Heng1

arXiv:1709.07330v2 [] 22 Nov 2017

1

Department of Computer Science and Engineering, The Chinese University of Hong Kong
2
Imsight Medical Technology, Inc

Abstract—Liver cancer is one of the leading causes of cancer
death. To assist doctors in hepatocellular carcinoma diagnosis and
treatment planning, an accurate and automatic liver and tumor
segmentation method is highly demanded in the clinical practice.
Recently, fully convolutional neural networks (FCNs), including
2D and 3D FCNs, serve as the back-bone in many volumetric
image segmentation. However, 2D convolutions can not fully
leverage the spatial information along the third dimension while
3D convolutions suffer from high computational cost and GPU
memory consumption. To address these issues, we propose a
novel hybrid densely connected UNet (H-DenseUNet), which
consists of a 2D DenseUNet for efficiently extracting intra-slice
features and a 3D counterpart for hierarchically aggregating
volumetric contexts under the spirit of the auto-context algorithm
for liver and tumor segmentation. We formulate the learning
process of H-DenseUNet in an end-to-end manner, where the
intra-slice representations and inter-slice features can be jointly
optimized through a hybrid feature fusion (HFF) layer. We
extensively evaluated our method on the dataset of MICCAI
2017 Liver Tumor Segmentation (LiTS) Challenge. Our method
outperformed other state-of-the-arts on the segmentation results
of tumors and achieved very competitive performance for liver
segmentation even with a single model.
Index Terms—CT, liver tumor segmentation, deep learning,
hybrid features

Raw image

Ground truth

3D display

Figure 1: Examples of contrast-enhanced CT scans showing
the large variations of shape, size, location of liver lesion. Each
row shows a CT scan acquired from individual patient. The
red regions denote the liver while the green ones denote the
lesions (see the black arrows above).

I. I NTRODUCTION
Liver cancer is one of the most common cancer diseases
in the world and causes massive deaths every year [1, 2].
The accurate measurements from CT, including tumor volume, shape, location and further functional liver volume, can
assist doctors in making accurate hepatocellular carcinoma
evaluation and treatment planning. Traditionally, the liver and
liver lesion are delineated by radiologists on a slice-by-slice
basis, which is time-consuming and prone to inter- and intrarater variations. Therefore, automatic liver and liver tumor
segmentation methods are highly demanded in the clinical
practice.
Automatic liver segmentation from the contrast-enhanced
CT volumes is a very challenging task due to the low intensity
contrast between the liver and other neighboring organs (see
the first row in Figure 1). Moreover, radiologists usually enhance CT scans by an injection protocol for clearly observing
tumors, which may increase the noise inside the images on
the liver region [3]. Compared with liver segmentation, liver
tumor segmentation is considered to be a more challenging
task. First, the liver tumor has various size, shape, location
and numbers within one patient, which hinders the automatic

segmentation, as shown in Figure 1. Second, some lesions do
not have clear boundaries, limiting the performance of solely
edge based segmentation methods (see the lesions in the third
row of Figure 1). Third, many CT scans consist of anisotropic
dimensions with high variations along the z-axis direction (the
voxel spacing ranges from 0.45mm to 6.0mm), which further
poses challenges for automatic segmentation methods.
To tackle these difficulties, the remarkable ability for extracting visual features is required. Recently, fully convolutional neural networks (FCNs) have achieved great success
on a broad array of recognition problems [4–12]. Many
researchers advance this stream using deep learning methods
in the liver and tumor segmentation problem and the literature
can be classified into two categories broadly. (1) 2D FCNs,
such as UNet architecture [13], the multi-channel FCN [14],
and the FCN based on VGG-16 [15]. (2) 3D FCNs, where 2D
convolutions are replaced by 3D convolutions with volumetric
data input [16, 17].
In clinical diagnosis, an experienced radiologist usually
observes and segments tumors according to many adjacent

2

slices along the z-axis. However, 2D FCN based methods
ignore the contexts on the z-axis, which would lead to limited
segmentation accuracy. To be specific, single or three adjacent slices cropped from volumetric images are fed into 2D
FCNs [14, 15] and the 3D segmentation volume is generated
by simply stacking the 2D segmentation maps. Although
adjacent slices are employed, it is still not enough to probe
the spatial information along the third dimension, which may
degrade the segmentation performance. To solve this problem,
some researchers proposed to use tri-planar schemes [4], that
is, three 2D FCNs are applied on orthogonal planes (e.g.,
the xy, yz, and xz planes) and voxel prediction results are
generated by the average of these probabilities. However,
this approach still cannot probe sufficient volumetric contexts,
leading to limited representation ability.
Compared to 2D FCNs, 3D FCNs suffer from high computational cost and GPU memory consumption. The high
memory consumption limits the depth of the network as well
as the filter’s field-of-view, which are the two key factors
for performance gains [18]. The heavy computation of 3D
convolutions also impedes its application in training a largescale dataset. Moreover, many researchers have demonstrated
the effectiveness of knowledge transfer (the knowledge learnt
from one source domain efficiently transferred to another
domain) for boosting the performance [19, 20]. Unfortunately,
only a dearth of 3D pre-trained model exists, which restricts
the performance and also the adoption of 3D FCNs.
To address the above problems, we proposed a novel endto-end system, called hybrid densely connected UNet (HDenseUNet), where intra-slice features and 3D contexts are
effectively probed and jointly optimized for accurate liver
and lesion segmentation. Our H-DenseUNet pushes the limit
further than other works with technical contributions on the
following two key factors:
Increased network depth. First, to fully extract high-level
intra-slice features, we design a very deep and efficient training
network based on 2D convolutions, called 2D DenseUNet,
which inherits the advantages of both densely connected
path [21] and UNet connections [5]. Densely connected path
is derived from densely connected network (DenseNet), where
the improved information flow and parameters efficiency
make it easy for training a deep network. Different from
DenseNet [21], we add the UNet connections, i.e., long-range
skip connections, between the encoding part and the decoding
part in our architecture; hence, we can enable low-level spatial
feature preservation for better intra-slice context exploration.
Hybrid feature exploration. Second, to explore the volumetric feature representation, we design an end-to-end training
system, called H-DenseUNet, where intra-slice and inter-slice
features are effectively extracted and then jointly optimized
through the hybrid feature fusion (HFF) layer. Specifically, 3D
DenseUNet is integrated with the 2D DenseUNet by the way
of auto-context mechanism. With the guidance of semantic
probabilities from 2D DenseUNet, the optimization burden in
the 3D DenseUNet can be well alleviated, which contributes
to the training efficiency for 3D contexts extraction. Moreover,
with the end-to-end system, the hybrid feature, consisting of
volumetric features and the high-level representative intra-slice

features, can be automatically fused and jointly optimized
together for better liver and tumor recognition.
In summary, this work has the following achievements:
• We design a DenseUNet to effectively probe hierarchical
intra-slice features for liver and tumor segmentation,
where the densely connected path and UNet connections
are carefully integrated to improve the performance. To
our knowledge, this is the first work that pushes the depth
of 2D networks for liver and tumor segmentation to 167
layers.
• We propose a H-DenseUNet framework to explore hybrid
(intra-slice and inter-slice) features for liver and tumor
segmentation, which elegantly tackles the problems that
2D convolutions neglect the volumetric contexts and 3D
convolutions suffer from heavy computational cost.
• Our framework is an end-to-end system that jointly fuses
and optimizes the hybrid features through the HFF layer,
which can be served as a new paradigm for effectively
exploiting 3D contexts. Compared with other state-ofthe-art methods, our method ranked the 1st on lesion
segmentation and achieved very competitive performance
on liver segmentation in the 2017 LiTS Leaderboard.
II. R ELATED W ORK
A. Hand-crafted feature based methods
In the past decades, a lot of algorithms, including thresholding [22, 23], region growing, deformable model based methods [24, 25] and machine learning based methods [26–30] have
been proposed to segment liver and liver tumor. Thresholdbased methods classified foreground and background according to whether the intensity value is above a threshold. Variations of region growing algorithms were also popular in the
liver and lesion segmentation task. For example, Wong et al.
[24] segmented tumors by a 2D region growing method with
knowledge-based constraints. Level set methods also attracted
attentions from researchers with the advantages of numerical
computations involving curves and surfaces [31]. For example,
Jimenez-Carretero et al. [25] proposed to classify tumors by
a multi-resolution 3D level set method coupled with adaptive
curvature technique. A large variety of machine learning based
methods have also been proposed for liver tumor segmentation.
For example, Huang et al. [26] proposed to employ the random
feature subspace ensemble-based extreme learning machine
(ELM) for liver lesion segmentation. Vorontsov et al. [27]
proposed to segment tumors by support vector machine (SVM)
classifier and then refined the results by the omnidirectional
deformable surface model. Similarly, Kuo et al. [29] proposed
to learn SVM classifier with texture feature vector for liver
tumor segmentation. Le et al. [28] employed the fast marching
algorithm to generate initial regions and then classified tumors
by training a noniterative single hidden layer feedforward
network (SLFN). To speed up the segmentation algorithm,
Chaieb et al. [32] adopted a bootstrap sampling approach for
efficient liver tumor segmentation.
B. Deep learning based methods
Convolutional neural networks (CNNs) have achieved great
success in many object recognition problems in computer

3

vision community. Many researchers followed this trend and
proposed to utilize various CNNs for learning feature representations in the application of liver and lesion segmentation.
For example, Ben-Cohen et al. [15] proposed to use a FCN
for liver segmentation and liver-metastasis detection in CT
examinations. Christ et al. [13, 33] proposed a cascaded
FCN architecture and dense 3D conditional random fields
(CRFs) to automatically segment liver and liver lesions. In the
meanwhile, Sun et al. [14] designed a multi-channel FCN to
segment liver tumors from CT images, where the probability
maps were generated by the feature fusion from different
channels.
Recently, during the 2017 ISBI LiTS challenge, Han [34],
proposed a 2.5D 24-layer FCN model to segment liver tumors,
where the residual block was employed as the repetitive
building blocks and the UNet connection was designed across
the encoding part and decoding part. 2.5D refers to using 2D
convolutional neural network with the input of adjacent slices
from the volumetric images. Both Vorontsov et al. [35] and
Chlebus et al. [36] achieved the second place in the ISBI challenge. Vorontsov et al. [35] also employed ResNet-like residual
blocks and UNet connections with 21 convolutional layers,
which is a bit shallower and has fewer parameters compared
to that proposed by Han [34]. Chlebus et al. [36] designed
a 28-layer UNet architecture in two individual models and
subsequently filtered the false positives of tumor segmentation
results by a random forest classifier. Instead of using 3D
FCNs, all of the top results employed 2D FCNs with different
network depths, showing the efficacy of 2D FCNs regarding
the underlying volumetric segmentation problem. However, all
these networks are shallow and ignore the 3D contexts, which
limit the high-level feature extraction capability and restrict
the recognition performance.
III. M ETHOD
Figure 2 shows the pipeline of our proposed method for liver
and tumor segmentation. To reduce the overall computation
time, a simple ResNet architecture [34] is trained to get a
quick but coarse segmentation of liver. With the region of
interest (ROI), our proposed H-DenseUNet efficiently probes
intra-slice and inter-slice features through a 2D DenseUNet
f2d and a 3D counterpart f3d , followed by jointly optimizing
the hybrid features in the hybrid feature fusion (HFF) layer
for accurate liver and lesion segmentation.
A. Deep 2D DenseUNet for Intra-slice Feature Extraction
The intra-slice feature extraction part follows the structure
of DenseNet-161 [21], which is composed of repetitive densely
connected building blocks with different output dimensions.
In each densely connected building block, there are direct
connections from any layer to all subsequent layers, as shown
in Figure 2(c). Each layer produces k feature maps and k is
called growth rate. One advantage of the dense connectivity
between layers is that it has fewer output dimensions than
traditional networks, avoiding learning redundant features.
Moreover, the densely connected path ensures the maximum
information flow between layers, which improves the gradient

flow, and thus alleviates the burden in searching for the optimal
solution in a very deep neural network.
However, the original DenseNet-161 [21] is designed for
the object classification task while our problem belongs to
the segmentation topics. Moreover, a deep FCN network for
segmentation tasks actually contains several max-pooling and
upsampling operations, which may lead to the information loss
of low-level (i.e., high resolution) features. Given above two
considerations, we develop a 2D DenseUNet, which inherits
both advantages of densely connected path and UNet-like connections [5]. Specifically, the dense connection between layers
is employed within each micro-block to ensure the maximum
information flow while the UNet long range connection links
the encoding part and the decoding part to preserve low-level
information.
Let I ∈ Rn×224×224×12×1 denote the input training samples
(for 224 × 224 × 12 input volumes) with ground-truth labels
Y ∈ Rn×224×224×12×1 , where n denotes the batch size of
the input training samples and the last dimension denotes the
channel. Yi,j,k = c since each pixel (i, j, k) is tagged with
class c (background, liver and tumor). Let function F denote
the transformation from the volumetric data to three adjacent
slices. Specifically, every three adjacent slices along z-axis are
stacked together and the number of groups can be transformed
to the batch dimension. For example, I2d = F (I), where
I2d ∈ R12n×224×224×3 denotes the input samples of 2D
DesneUNet. The detailed transformation process is illustrated
in Figure 2(d). Because of the transformation, the 2D and 3D
DenseUNet can be jointly trained, which will be described in
detail in section B. For convenience, F −1 denotes the inverse
transformation from three adjacent slices to the volumetric
data. The 2D DenseUNet conducts liver and tumor segmentation,
X2d = f2d (I2d ; θ2d ), X2d ∈ R12n×224×224×64 ,
yˆ2d = f2dcls (X2d ; θ2dcls ), yˆ2d ∈ R12n×224×224×3

(1)

where X2d is the feature map from layer "upsample5_conv"
(see Table I) and yˆ2d is the predicted pixel-wise probabilities
corresponding to the input three adjacent slices.
The illustration and detailed structure of 2D DenseUNet
are shown in Figure 2(c) and Table I, respectively. The depth
of 2D DenseUNet is extended to 167 layers, referred as 2D
DenseUNet-167, which consists of convolution layers, pooling
layers, dense blocks, transition layers and upsampling layers.
The dense block denotes the cascade of several micro-blocks,
in which all layers are directly connected, see Figure 2(c).
To change the size of feature-maps, the transition layer is
employed, which consists of a batch normalization layer and
a 1 × 1 convolution layer followed by an average pooling
layer. A compression factor is included in the transition
layer to compress the number of feature-maps, preventing the
expanding of feature-maps (set as 0.5 in our experiments). The
upsampling layer is implemented by the bilinear interpolation,
followed by the summation with low-level features (i.e., UNet
connections) and a 3 × 3 convolutional layer. Before each
convolution layer, the batch normalization and the Rectified
Linear Unit (ReLU) are employed in the architecture.

4

,

+
2D
DenseUNet

3D
DenseUNet

′

,

2D input: 224

224

Concatenation

3

HFF layer

Liver segmentation Tumor segmentation

padding

2D ResNet

12

(b) 2nd stage: H-DenseUNet for accurate liver and tumor segmentation

1
3
1
3

3

3

3

1

3

3

1

1
3

1
3
1
3

Upsampling layer 3 Upsampling layer 4 Upsampling layer 5

1
3

7
7

1
3

1
3

6

(d) The illustration of transformation process

1

Transition layer 1

Dense block 1

1

(a) 1st stage : Coarse liver segmentation

224

3

3D input: 224

(c) The illustration of the 2D DenseUNet.

Figure 2: The illustration of the pipeline for liver and lesion segmentation. (a) A ResNet is employed for coarse liver
segmentation to reduce computation time. (b) The H-DenseUNet consists of 2D DenseUNet and 3D counterpart, which are
responsible for hierarchical features extraction from intra-slice and inter-slice, respectively. Hybrid feature fusion (HFF) layer
is proposed to fuse intra-slice and inter-slice features and optimize together for better liver and tumor segmentation. L(y, yˆ2d 0 )
and L(y, yˆH ) are jointly employed to supervise this end-to-end learning process. (c) The illustration of the 2D DenseUNet.
The structure in the orange block is a micro-block and k denotes the growth-rate. (d) The transformation of volumetric data
to three adjacent slices (Best viewed in color).

B. H-DenseUNet for Hybrid Feature Exploration
2D DenseUNet with deep convolutions can produce highlevel representative in-plane features but neglect the spatial
information along the z dimension while 3D DenseUNet has
large GPU computational cost and limited kernel’s field-ofview as well as the network depth. To address these issues,
we propose H-DenseUNet to jointly fuse and optimize the
learned intra-slice and inter-slice features for better liver tumor
segmentation.
To fuse hybrid features from the 2D and 3D network, the
feature volume size should be aligned. Therefore, the feature
maps and score maps from 2D DenseUNet are transformed to
the volumetric shape as follows:
X2d 0 = F −1 (X2d ), X2d 0 ∈ Rn×224×224×12×64 ,
yˆ2d 0 = F −1 (yˆ2d ), yˆ2d 0 ∈ Rn×224×224×12×3 ,

(2)

Then the 3D DenseUNet distill the visual features with 3D
contexts by concatenating the original volumes I with the
contextual information yˆ2d 0 from the 2D network. Specifically,

the detectors in the 3D counterpart trained based not only
on the features probed from the original images, but also on
the probabilities of a large number of context pixels from 2D
DenseUNet. With the guidance from the supporting contexts
pixels, the burden in searching for the optimal solution in the
3D counterpart has also been well alleviated, which significantly improves the learning efficiency of the 3D network.
The learning process of 3D DenseUNet can be described as:
X3d = f3d (I, yˆ2d 0 ; θ3d ),
Z = X3d + X2d 0 ,

(3)

where X3d denotes the feature volume from layer "upsample5_conv" in 3D DenseUNet-65. Z denotes the hybrid feature,
which refers to the sum of intra-slice and inter-slice features
from 2D and 3D network, respectively. Then the hybrid feature
is jointly learned and optimized in the HFF layer,
H = fHF F (Z; θHF F ),
yˆH = fHF F cls (H; θHF F cls )

(4)

5

Table I: Architectures of the proposed H-DenseUNet, consisting of the 2D DenseUNet and the 3D counterpart. The symbol
[ ] denotes the long range UNet summation connections. The second and forth column indicate the output size of the current
stage in two architectures, respectively. Note that each "conv" corresponds the sequence BN-ReLU-Conv.
input
convolution 1
pooling

Feature size
224 × 224
112 × 112
56 × 56

dense block 1

56 × 56

transition layer 1

56 × 56
28 × 28

dense block 2

28 × 28

transition layer 2

28 × 28
14 × 14

dense block 3

14 × 14

transition layer 3

14 × 14
7×7
7×7

dense block 4
upsampling layer
upsampling layer
upsampling layer
upsampling layer
upsampling layer
convolution 2

1
2
3
4
5

14
28
56
112
224
224

×
×
×
×
×
×

14
28
56
112
224
224

2D DenseUNet-167 (k=48)
7 × 7,96,stride 2
 3 × 3 max pool,stride
 2
1 × 1, 192 conv
×6
3 × 3, 48
conv
1 × 1 conv
2 × 2 average pool


1 × 1, 192 conv
× 12
3 × 3, 48
conv
1 × 1 conv
 2 × 2 average pool,

1 × 1, 192 conv
× 36
3 × 3, 48
conv
1 × 1 conv
2 × 2 average pool


1 × 1, 192 conv
× 24
3 × 3, 48
conv
2 × 2 upsampling [dense block 3], 768, conv
2 × 2 upsampling [dense block 2], 384, conv
2 × 2 upsampling [dense block 1], 96, conv
2 × 2 upsampling [convolution 1], 96, conv
2 × 2 upsampling, 64, conv
1 × 1, 3

where H denotes the optimized hybrid features and yˆH refers
to the pixel-wise predicted probabilities generated from the
HFF layer fHF F cls (·). In our experiments, the 3D counterpart
of H-DenseUNet cost only 9 hours to converge, which is
significantly faster than training the 3D counterpart with
original data solely (63 hours).
The detailed structure of the 3D counterpart is shown in
the Table I, called 3D DenseUNet-65, which consists of 65
convolutional layers and the growth rate is 32. Compared
with 2D DenseUNet counterpart, the number of micro-blocks
in each dense block is decreased due to the high memory
consumption of 3D convolutions and the limited GPU memory.
The rest of the network setting is the same with the 2D
counterpart.
C. Loss Function, Training and Inference Schemes
In this section, we present more details regarding the loss
function, training and the inference schemes.
1) Loss Function: To train the networks, we employed
weighted cross-entropy function as the loss function, which
is described as:
L(y, ŷ) = −

N
3
1 XX c c
w y log yˆi c
N i=1 c=1 i i

(5)

where yˆi c denotes the probability of voxel i belongs to class
c (background, liver or lesion), wic denotes the weight and yic
indicates the ground truth label for voxel i.
2) Training Scheme: We first train the ResNet in the same
way with Han [34] to get the coarse liver segmentation results.
The parameters of the encoder part in 2D DenseUNet f2d
are initialized with DenseNet’s weights (object classificationtrained) [21] while the decoder part are trained with the
random initialization. Since the weights are initialized with a
random distribution in the decoder part, we first warm up the

Feature size
224 × 224 × 12
112 × 112 × 6
56 × 56 × 3
56 × 56 × 3
56 × 56 × 3
28 × 28 × 3
28 × 28 × 3
28 × 28 × 3
14 × 14 × 3
14 × 14 × 3
14 × 14 × 3
7×7×3
7×7×3
14 × 14 × 3
28 × 28 × 3
56 × 56 × 3
112 × 112 × 6
224 × 224 × 12
224 × 224 × 12

3D DenseUNet-65 (k=32)
7 × 7 × 7,96,stride
 3 × 3 × 3 max pool,stride
 2
1 × 1 × 1, 128 conv
×3
3 × 3 × 3, 32
conv
1 × 1 × 1 conv
 2 × 2 × 1 average pool

1 × 1 × 1, 128 conv
×4
3 × 3 × 3, 32
conv
1 × 1 × 1 conv
 2 × 2 × 1 average pool

1 × 1 × 1, 128 conv
× 12
3 × 3 × 3, 32
conv
1 × 1 × 1 conv
 2 × 2 × 1 average pool

1 × 1 × 1, 128 conv
×8
3 × 3 × 3, 32
conv
2 × 2 × 1 upsampling [dense block 3], 504, conv
2 × 2 × 1 upsampling [dense block 2], 224, conv
2 × 2 × 1 upsampling [dense block 1], 192, conv
2 × 2 × 2 upsampling [convolution 1], 96, conv
2 × 2 × 2 upsampling, 64, conv
1 × 1 × 1, 3

network without UNet connections. After several iterations,
the UNet connections are added to jointly fine tune the model.
To effectively train the H-DenseUNet, we first optimize
f2d (·) and f2dcls (·) with cross entropy loss L(y, yˆ2d 0 ) on our
dataset. Secondly, we fix parameters in f2d (·) and f2dcls (·),
and focus on training f3d (·), fHF F (·) and fHF F cls (·) with
cross entropy loss L(y, yˆH ), where parameters are all randomly initialized. Finally, The whole network is jointly finetuned with following combined loss:
Ltotal = λL(y, yˆ2d 0 ) + L(y, yˆH )

(6)

where λ is the balanced weight and set as 0.5 in our experiments empirically.
3) Inference Scheme: In the test stage, we first get the
coarse liver segmentation result. Then H-DenseUNet can generate accurate liver and tumor predicted probabilities within
the ROI. The thresholding is applied to get the liver tumor
segmentation result. To avoid the holes in the liver, a largest
connected component labeling is performed to refine the
liver result. After that, the final lesion segmentation result is
obtained by removing lesions outside the final liver region.
IV. E XPERIMENTS AND R ESULTS
A. Dataset and Pre-processing
We tested our method on a competitive dataset of MICCAI
2017 LiTS Challenge, which contains 131 and 70 contrastenhanced 3D abdominal CT scans for training and testing,
respectively. The dataset was acquired by different scanners
and protocols from six different clinical sites, with a largely
varying in-plane resolution from 0.55 mm to 1.0 mm and slice
spacing from 0.45 mm to 6.0 mm.
For image preprocessing, we truncated the image intensity
values of all scans to the range of [-200,250] HU to remove
the irrelevant details. For coarse liver segmentation, all the

6

3D DenseUNet without pre-trained model
2D DenseUNet without pre-trained model
2D DenseNet with pre-trained model
2D DenseUNet with pre-trained model
H-DenseUNet

0.35
0.30

loss

0.25
0.20
0.15
0.10
0.05
0

20

40

epoch

60

80

100

Figure 3: Training losses of 2D DenseUNet with and without pre-trained model, 2D DenseNet with pre-trained model,
3D DenseUNet without pre-trained model as well as HDenseUNet (Best viewed in color).

training images were resampled to a fixed resolution of 0.69×
0.69 × 1.0 mm3 . Since the liver lesions are extremely small
in some training cases, we trained with the original images in
the networks to avoid possible artifact from image resampling.
B. Evaluation Metrics
According to the evaluation of 2017 LiTS challenge, we
employed Dice per case score and Dice global score to evaluate the liver and tumor segmentation performance respectively.
Dice per case score refers to an average Dice score per
volume while Dice global score is the Dice score evaluated
by combining all datasets into one. Root mean square error
(RMSE) is also adopted to measure the tumor burden.
C. Implementation Details
In this section, we present more details regarding the
implementation environment and data augmentation strategies.
The model was implemented using Keras package [37]. The
initial learning rate was 0.01 and decayed according to the
equation lr = lr ∗ (1 − iterations/total_iterations)0.9 . We
used stochastic gradient descent with momentum.
For data augmentation, we adopted random mirror and
scaling between 0.8 and 1.2 for all training data to alleviate
the overfitting problem. The training of 2D DenseUNet model
took about 21 hours using two NVIDIA Titan Xp GPUs with
12 GB memory while the 3D counterpart model cost about 9
hours under the same settings. In other words, the total training
time for H-DenseUNet took about 30 hours. In the test phase,
the total processing time of one subject depends on the number
of slices, ranging from 30 seconds to 200 seconds.
D. Ablation Analysis of H-DenseUNet
In this section, we conduct comprehensive experiments to
analyze the effectiveness of our proposed H-DenseUNet.

1) Effectiveness of the Pre-trained Model: One advantage
in the proposed method is that we can train the network by
transfer learning with the pre-trained model, which is crucial in
finding an optimal solution for the network. Here, we analyze
the learning behaviors of 2D DenseUNet with and without
the pre-trained model. Both two experiments were conducted
under the same experimental settings. From Figure 3, it is
clearly observed that with the pre-trained model, 2D DenseUNet can converge faster and achieve lower loss value, which
shows the importance of utilizing the pre-trained model with
transfer learning. The test results in Table II demonstrated
that the pre-trained model can help the network achieve better
performance consistently. Our proposed H-DenseUNet inherits
this advantage, which plays an important role in achieving the
promising results.
2) Comparison of 2D and 3D DenseUNet: We compare the
inherent performance of 2D DenseUNet and 3D DenseUNet
to validate that using 3D network solely maybe defective. The
number of parameters is one of key elements in measuring the
model representation capability, thus both 2D DesneUNet-167
and 3D DenseUNet-65 are designed with the same level of
model complexity (around 40M parameters).
We compare the learning behaviors of two experiments
without using the pre-trained model. From Figure 3, it shows
that the 2D DenseUNet achieves better performance than
the 3D DenseUNet, which highlights the effectiveness and
efficiency of 2D convolutions with the deep architecture. This
is because the 3D kernel consumes large GPU memory so
that the network depth and width are limited, leading to weak
representation capability. In addition, 3D DenseUNet took
much more training time (63 hours) to converge compared
to 2D DenseUNet (21 hours).
Except for the heavy computational cost of the 3D network,
another defective is that only a dearth of pre-trained model
exists for the 3D network. From Table II, compared with the
results generated by 3D DenseUNet, 2D DenseUNet with pretrained model achieved 8.9 and 3.0 (Dice: %) improvements
on the lesion segmentation results by the measurement of Dice
per case and Dice global score, respectively.
3) Effectiveness of UNet Connections: We analyze the
effectiveness of UNet connections in our proposed framework.
Both 2D DenseNet and DenseUNet are trained with the same
pre-trained model and training strategies. The difference is
that DenseUNet contains long range connections between
the encoding part and the decoding part to preserve highresolution features. As the results shown in Figure 3, it
is obvious that DenseUNet achieves lower loss value than
DenseNet, demonstrating the UNet connections actually help
the network converge to a better solution. The experimental
results in Table II consistently demonstrated that the lesion
segmentation performance can be boosted by a large margin
with UNet connections embedded in the network.
4) Effectiveness of Hybrid Feature Fusion: To validate
the effectiveness of the hybrid architecture, we compare the
learning behaviors of H-DenseUNet and 2D DenseUNet. It is
observed that the loss curve for H-DenseUNet begins around
0.04. This is because we fine tune the H-DenseUNet on the 2D
DenseUNet basis, which serves as a good initialization. Then

7

Table II: Segmentation results by ablation study of our methods on the test dataset (Dice: %).
Model
3D DenseUNet without pre-trained model
UNet [36]
ResNet [34]
2D DenseUNet without pre-trained model
2D DenseNet with pre-trained model
2D DenseUNet with pre-trained model
H-DenseUNet

Lesion
Dice per case
Dice global
59.4
78.8
65.0
67.0
67.7
80.1
68.3
81.8
70.2
82.1
72.2
82.4

Liver
Dice per case
Dice global
93.6
92.9
94.7
94.7
95.3
95.9
95.8
96.3
96.1
96.5

Table III: Leaderboard of 2017 Liver Tumor Segmentation (LiTS) Challenge (Dice: %, until 1st Nov. 2017)
Team
our
IeHealth
hans.meine
superAI
Elehanx [34]
medical
deepX [38]
Njust768
Medical [35]
Gchlebus [36]
predible
Lei [39]
ed10b047
chunliang
yaya

Lesion
Dice per case
Dice global
72.2
82.4
70.2
79.4
67.6
79.6
67.4
81.4
67.0
66.1
78.3
65.7
82.0
65.5
76.8
65.0
65.0
64.0
77.0
64.0
63.0
77.0
62.5
78.8
62.4
79.2

Liver
Dice per case
Dice global
96.1
96.5
96.1
96.4
96.0
96.5
0.0
0.0
95.1
95.1
96.3
96.7
4.10
13.5
95.0
95.0
94.0
94.0
95.8
96.2
95.9
96.3

Tumor Burden
RMSE
0.015
0.017
0.020
1251.447
0.023
0.017
0.920
0.020
0.020
0.016
0.016

Note: - denotes that the team participated in ISBI competition and the measurement was not evaluated.

case 1

case 37
Raw image

Ground truth

2D DenseUNet

H-DenseUNet

Figure 4: Examples of segmentation results by 2D DenseUNet
and H-DenseUNet on the validation dataset. The red regions
denote the segmented liver while the green ones denote the
segmented lesions. The gray regions denote the true liver while
the white ones denote the true lesions.

the loss value decreases to nearly 0.02, which is attributed
to the hybrid feature fusion learning. Figure 3 shows that
H-DenseUNet can converge to the smaller loss value than
the 2D DenseUNet, which indicates that the hybrid architecture can contribute to the performance gains. Compared
with 2D DenseUNet, our proposed H-DenseUNet advances
the segmentation results on both two measurements for liver
and tumor segmentation consistently, as shown in Table II.
The performance gains indicate that contextual information
along the z dimension, indeed, contributes to the recognition
of lesion and liver, especially for lesions that have much more
blurred boundary and considered to be difficult to recognize.

Figure 5: Examples of liver and tumor segmentation results of
H-DenseUNet from the test dataset. The red regions denote
the liver and the green ones denote the tumors.
case 69

Figure 4 shows some segmentation results achieved by 2D
DenseUNet and H-DenseUNet on the validation dataset. It is
observed that H-DenseUNet can achieve much better results
than 2D DenseUNet. Moreover, we trained H-DenseUNet in
an end-to-end manner, where the 3D contexts can also help
extract more representative in-plane features. The end-to-end
system jointly optimizes the 2D and 3D networks, where the
hybrid feature can be fully explored. Figure 5 presents some
examples of liver and tumor segmentation results of our HDenseUNet on the test dataset. We can observe that most small
targets as well as large objects can be well segmented.

8

E. Comparison with Other Methods

2000000

There were more than 50 submissions in 2017 ISBI and
MICCAI LiTS challenges. Both challenges employed the same
training and test datasets for fair performance comparison.
Different from the ISBI challenge, more evaluation metrics
have been added in the MICCAI challenge for comprehensive
comparison. The detailed results of top 15 teams on the
leaderboard, including both ISBI and MICCAI challenges, are
listed in Table III. Our method outperformed other state-of-thearts on the segmentation results of tumors and achieved very
competitive performance for liver segmentation. For tumor
burden evaluation, our method achieved the lowest estimation
error and ranked the 1st place among all the teams. It is worth
mentioning that our result was achieved by a single model.
Most of the top teams in the challenges employed deep
learning based methods, demonstrating the effectiveness of
CNN based methods in medical image analysis. For example,
Han [34], Vorontsov et al. [35] and Bi et al. [39] all adopted 2D
deep FCNs, where ResNet-like residual blocks were employed
as the building blocks. In addition, Chlebus et al. [36] trained
the UNet architecture in two individual models, followed by
a random forest classifier. In comparison, our method with a
167-layer network consistently outperformed these methods,
which highlighted the efficacy of 2D DenseUNet with pretrained model. Our proposed H-DenseUNet further advanced
the segmentation accuracy for both liver and tumor, showing
the effectiveness of the hybrid feature learning process.
Our method achieved the 1st place among all state-of-thearts in the lesion segmentation and very competitive result
to DeepX [38] for liver segmentation. Note that our method
surpassed DeepX by a significant margin in the Dice per case
evaluation for lesion, which is considered to be notoriously
challenging and difficult. Moreover, our result was produced
by the single model while DeepX [38] employed multi-model
combination strategy to improve the results, showing the
efficiency of our method in the clinical practice.

1750000

V. D ISCUSSION
Automatic liver and tumor segmentation plays an important
role in clinical diagnosis. It provides the precise contour of
the liver and any tumors inside the anatomical segments of
the liver, which assists doctors in the diagnosis process. In
this paper, we present an end-to-end training system to explore hybrid features for automatic liver lesion segmentation,
where we probe 3D contexts effectively under the auto-context
mechanism. Through the hybrid fusion learning of intraslice and inter-slice features, the segmentation performance
for liver lesion has been improved, which demonstrates the
effectiveness of our H-DenseUNet. Moreover, compared with
other 3D networks [9, 16], our method probes 3D contexts
efficiently. This is crucial in the clinical practice, especially
when huge amount of 3D images, containing large image size
and a number of slices, are increasingly accumulated in the
clinical sites.
To have a better understanding about the performance gains,
we analyze the effectiveness of our method regarding the
tumor size in each patient. Figure 6 shows the tumor size

Boundary for the large and small tumor groups

1500000

Tumor size

1250000
1000000
750000
500000
250000
0
0

5

10

15

20
Image id

25

30

35

40

Figure 6: Tumor size in each patient of the validation dataset.
Table IV: Effectiveness of our method regarding to the tumor
size (Dice: %).
Baseline
H-DenseUNet

Total
43.56
45.04 (+1.48)

Large-tumor group
58.24
60.59 (+2.35)

Small-tumor group
41.08
42.18 (+1.1)

Note: Baseline is the 2D DenseUNet with pre-trained model.

value of 40 CT volume data in the validation dataset, where
the tumor size is obtained by summing up tumor voxels in each
ground-truth image. It is observed that the dataset has large
variations of the tumor size. For comparison, we divide the
dataset into the large-tumor group and the small-tumor group
by the orange line in Figure 6. From Table IV, we can observe
that our method improves the segmentation accuracy by 1.48
(Dice:%) in the whole validation dataset. We can also observe
that the large-tumor group achieves 2.35 (Dice:%) accuracy
improvements while the score for the small-tumor group is
slightly advanced, with 1.1 (Dice:%). From the comparison,
we claim that the performance gain is mainly attributed to
the improvement of the large-tumor data segmentation results.
This is mainly because that the H-DenseUNet mimics the
diagnosis process of radiologists, where tumors are delineated
by observing several adjacent slices, especially for tumors
have blurred boundaries. Once the blurred boundaries are well
segmented, the segmentation accuracy for the large-tumor data
can be improved by a large margin. Although the hybrid
feature still contributes to the segmentation of small tumors,
the improvement is limited since small tumors usually occur in
fewer slices. In the future, we will focus on the segmentation
for small tumors. Several potential directions will be taken into
considerations, i.e., multi-scale representation structure [40]
and deep supervision [16]. Recently, perceptual generative
adversarial network (GAN) [41] has been proposed for small
object detection. They generate superresolved representations
for small objects by discovering the intrinsic structural correlations between small-scale and large-scale objects, which
may also be a potential direction for handling this challenging
problem.
Another key that should be explored in the future study is

9

the potential depth for the H-DenseUNet. In our experiments,
we trained the network using data parallel training, which is
an effective technique to speed up the gradient descent by
paralleling the computation of the gradient for a mini-batch
across mini-batch elements. However, the model complexity
is restricted by the GPU memory. In the future, to exploit
the potential depth of the H-DenseUNet, we can train the
network using model parallel training, where different portions
of the model computation are done on distributed computing
infrastructures for the same batch of examples, which will be
another possible direction to further improve the performance.

[7]

[8]

VI. C ONCLUSION
We present an end-to-end training system H-DenseUNet for
liver and tumor segmentation from CT volumes, which is a
new paradigm to effectively probe high-level representative
intra-slice and inter-slice features, followed by optimizing
the features through the hybrid feature fusion layer. The
architecture gracefully addressed the problems that 2D convolutions ignore the volumetric contexts and 3D convolutions
suffer from heavy computational cost. Extensive experiments
on the dataset of 2017 LiTS demonstrated the superiority
of our proposed H-DenseUNet. With a single-model basis,
our method excelled others by a large margin on lesion
segmentation and achieved very competitive result on liver
segmentation on the Leaderboard. In addition, our network
architecture is inherently general and can be easily extended
to other applications.
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