Liver tumor segmentation
63599761
Intelligent Algorithm for Liver Tumor CT Image Segmentation
A novel method for liver tumor CT image segmentation was proposed in this paper.By utilizing the high level knowledge of medical experts,the method could segment the image more accurately and faster.Combining a multi scale watershed algorithm and fuzzy C means clustering,an optimal result could be achieved.The traditional FCM algorithm was modified by introducing spatial information in the optimization function,and its convergence was proven.Experimental results showed that the modified algorithm performs better than the traditional one in its correctness and compactness of clustering.
Dong-Mei Yu Chen Xi
1627955
Liver Segmentation in CT Data: A Segmentation Refinement Approach
Liver segmentation is an important prerequisite for planning of surgical interventions like liver tumor resections. For clinical applicability, the segmentation approach must be able to cope with the high variation in shape and gray value appearance of the liver. In this paper we present a novel segmentation scheme based on a true 3D segmentation refinement concept utilizing a hybrid desktop/virtual reality user interface. The method consists of two main stages. First, an initial segmentation is generated using graph cuts. Second, an interactive segmentation refinement step allows a user to fix arbitrary segmentation errors. We demonstrate the robustness of our method on ten contrast enhanced liver CT scans. Our segmentation approach copes successfully with the high variation found in patient data sets and allows to produce segmentations in a time efficient manner.
Reinhard R Beichel Christian Bauer Alexander Bornik E Sorantin Horst Bischof
46124981
Liver Segmentation Based on Expectation Maximization and Morphological Filters in CT Images
Segmentation of liver tissues in CT image is particularly challenging due to the anatomic complexity. An integrated model based statistical learning and morphology operations are presented in the paper to simplify the liver segmentation procedure and achieve qualified results simultaneously. The proposed scheme consists of two subroutines: initial segmentation and refinement. The former estimates statistical parameter vector of mixture Gaussian distribution by the EM algorithm and take initial classification based on maximum probabilities. The latter makes further refinement using morphological filters to remove foreign components and apply hole filling routine. Experimental results show the ability of the proposed algorithm to accurately segment the liver structure in presence of liver tumor and other anatomic organs, and suggest its suitability to other medical image segmentation tasks.
2007 1st International Conference on Bioinformatics and Biomedical Engineering
Li Ma L Yang
44415093
Adaptive Level Set Method for Segmentation of Liver Tumors in Minimally Invasive Surgery Using Ultrasound Images
Ultrasound images have been employed in guiding clinical interventional therapy procedures for liver tumor. However, segmenting liver tumor in the ultrasound images presents a unique challenge because of the low contrast objects in the noisy image. Snakes, or active contours have had limited success in such noisy and complex image. In this paper, an adaptive level set method is proposed, which combines the global statistics and boundary statistics instead of image gradient and edge strength .Compared to traditional level set method, the experiment results show that the proposed level set method was feasible enabled accurate and robust.
2007 1st International Conference on Bioinformatics and Biomedical Engineering
Jing Xu K Chen Xiangdong Yang D Wu Senqiang Zhu
17575150
Segmentation of liver region with tumorous tissues
Segmentation of an abnormal liver region based on CT or MR images is a crucial step in surgical planning. However, precisely carrying out this step remains a challenge due to either connectivities of the liver to other organs or the shape, internal texture, and homogeneity of liver that maybe extensively affected in case of liver diseases. Here, we propose a non density based method for extracting the liver region containing tumor tissues by edge detection processing. False extracted regions are eliminated by a shape analysis method and thresholding processing. If the multi phased images are available then the overall outcome of segmentation can be improved by subtracting two phase images, and the connectivities can be further eliminated by referring to the intensity on another phase image. Within an edge liver map, tumor candidates are identified by their different gray values relative to the liver. After elimination of the small and nonspherical over extracted regions, the final liver region integrates the tumor region with the liver tissue. In our experiment, 40 cases of MDCT images were used and the result showed that our fully automatic method for the segmentation of liver region is effective and robust despite the presence of hepatic tumors within the liver.
SPIE Medical Imaging
Xuejun Zhang Gobert N Lee Tetsuji Tajima Teruhiko Kitagawa Masayuki Kanematsu Xiangrong Zhou Takeshi Hara Hiroshi Fujita Ryujiro Yokoyama Hiroshi Kondo Hiroaki Hoshi Shigeru Nawano Kenji Shinozaki
17197706
Graph Cut Liver Segmentation for Interstitial Ultrasound Therapy
Within a specific medical application, the primary liver cancer curative treatment by a percutaneous high intensity ultrasound surgery, our study was designed to propose a fast 3D semi automatic segmentation method of the liver, the tumor and the hepatic vascular networks. This method is characterized by a graph description of contrast medium injected CT volume where the links between the nodes describe either the degrees of similarity between voxels of the same class (region based approach) or the class changes between two neighboring voxels (boundary based approaches) The various weights describing these two properties are defined after a first interactive training phase. A Max Flow/Min Cut graph cut algorithm allowed partitioning the volume in two representative subsets of the segmented classes.
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Simon Esneault Najah Hraiech Eric Delabrousse Jean-Louis Dillenseger
9260285
Pre operative estimation of esophageal tumor metabolic length in FDG PET images with surgical pathology confirmation
ObjectiveThe aim of this study was to evaluate a 3D tumor segmentation method for fluorodeoxyglucose positron emission tomography (FDG PET) in the context of noninvasive estimation of tumor metabolic length (Lm) as it correlates with surgical pathology and phantom results.MethodsThirty four patients (7 women, 27 men) with esophageal cancer were retrospectively evaluated. All patients underwent FDG PET computed tomography (CT) imaging following endoscopic ultrasound (EUS) Seventeen patients had esophagectomy after PET/CT, without prior neoadjuvant therapy. Tumor length was assessed by EUS (Le, n 31) and histopathology (Lp, n 17) Images were evaluated quantitatively with a 3D threshold based region growing program (Medical Image Processing Analysis and Visualization) Lm, total metabolic volume (Vm) maximum standardized uptake value (SUVmax) and average SUV (SUVa) over the entire tumor were obtained for several threshold values (mean activity in the liver plus 0 1 2 3 and 4 SD of the activity in the liver).ResultsLm showed a good correlation with Lp for all thresholds (best correlation for Lm(2 SD) r 0.74, P 0.001) A positive nonsignificant correlation was observed between Lp and Le (r 0.30, P 0.29) Lm(2 SD) correlated well with Le (r 0.71, P 0.001) Good correlations were also observed between Lm(2 SD) and Vm(2 SD) (r 0.89, P 0.001) and SUVa(2 SD) (r 0.38, P 0.05) Vm(2 SD) also had a significant correlation with Lp (r 0.61, P 0.05) and Le (r 0.57, P 0.001).ConclusionsFDG PET derived tumor metabolic length of untreated esophageal carcinomas correlates well with surgical pathology results, and provides preliminary evidence that noninvasive delineation of the superior and inferior extent of viable tumor involvement might be feasible using computer generated metabolic length measurements.
Annals of nuclear medicine
Marcelo Mamede Georges El Fakhri Paula Abreu-e-Lima William Gandler Vania Nose Victor H Gerbaudo
70946858
Segmentation of Costal Cartilage in Abdominal CT Data using Watershed Markers
High Intensity Focused Ultrasound (HIFU) ablation is a promising non invasive technique that heats a specific tumor region to fatal levels, while minimizing cell death in nearby healthy areas. For liver applications, the rib cage limits the transducer placement. A treatment plan based on CT images would segment the ribs and provide visualization of them and the tumor. A HIFU simulation of deposited heat would also require rib segmentation. Unfortunately, the ribs are difficult to segment on CT as they transition to cartilage, with CT units similar to that of the liver. The purpose of this work was to develop a rib segmentation algorithm based on CT images for HIFU treatment planning. After an initial threshold of the CT data, rib regions were characterized based on their size, and if a region were greater than a predetermined area parameter (i.e. it consisted of rib and liver) a marker based watershed transformation separated the two regions and continued to the next inferior slice. After false positives.
Andrew B Holbrook Kim Butts Pauly
73343427
Segmentation d'images scanner X du foie par Max Flow/Min Cut
Within a specific medical application, the primary liver cancer curative treatment by a percutaneous high intensity ultrasound surgery, our study was designed to propose a fast 3D semi automatic segmentation method of the liver, the tumor and the hepatic vascular networks. This method is characterized by a graph description of contrast medium injected CT volume where the links between the nodes describe either the degrees of similarity between voxels of the same class (region based approach) or the class changes between two neighboring voxels (boundary based approaches) The various weights describing these two properties are defined after a first interactive training phase. A Max Flow/Min Cut graph cut algorithm allowed partitioning the volume in two representative subsets of the segmented classes.
Kristell Solange Ladines Torres Thien-Ly Pham Simon Esneault Jean-Claude Nunes Eric Delabrousse Jean-Louis Dillenseger
21731868
Dynamic thresholding level set: a novel computer aided volumetry method for liver tumors in hepatic CT images
Measurement of the volume of focal liver tumors, called liver tumor volumetry, is indispensable for assessing the growth of tumors and for monitoring the response of tumors to oncology treatments. Traditional edge models, such as the maximum gradient and zero crossing methods, often fail to detect the accurate boundary of a fuzzy object such as a liver tumor. As a result, the computerized volumetry based on these edge models tends to differ from manual segmentation results performed by physicians. In this study, we developed a novel computerized volumetry method for fuzzy objects, called dynamic thresholding level set (DT level set) An optimal threshold value computed from a histogram tends to shift, relative to the theoretical threshold value obtained from a normal distribution model, toward a smaller region in the histogram. We thus designed a mobile shell structure, called a propagating shell, which is a thick region encompassing the level set front. The optimal threshold calculated from the histogram of the shell drives the level set front toward the boundary of a liver tumor. When the volume ratio between the object and the background in the shell approaches one, the optimal threshold value best fits the theoretical threshold value and the shell stops propagating. Application of the DT level set to 26 hepatic CT cases with 63 biopsy confirmed hepatocellular carcinomas (HCCs) and metastases showed that the computer measured volumes were highly correlated with those of tumors measured manually by physicians. Our preliminary results showed that DT level set was effective and accurate in estimating the volumes of liver tumors detected in hepatic CT images.
SPIE Medical Imaging
Wenli Cai Hiroyuki Yoshida Gordon J Harris