
# 3DL_NuCount model


__Model author__: Fabrice Daian


#### Model description

__3DL_NuCount__ model has been designed by fine tuning a pretrained Stardist3D model [1,2] using a home made dataset [3] in order to assess the number of cells present in a given 3D image stack acquired using an optical microscope. Training and Inference Notebooks are hosted on our Github repo [4].

#### Stardist Training parameters

- patch size:  (48,96,96)
- batch size:  32
- epochs    :  100
- data augmentation : flip/rotation/intensity
- image normalization: normalize channel independantly
- anisotropy:  empirical
- rays      :  96

#### Training dataset parameters
- tile size : (4,63,128,128)
- split     : Train 0.8 / Val 0.2

#### Inference
- patch size     : (784,784,:)
- image size     : (2048,2048,:)
- model name     : __weights_best_1.h5__
- config file    : __config.json__
- threshold file : __thresholds.json__


#### References
- [1] Stardist Project: [Github](https://github.com/stardist/stardist)
- [2] Stardist Paper :  [ArXiv](https://arxiv.org/abs/1908.03636)
- [3] NuCount Training Dataset  : [Zenodo](https://)
- [4] NuCount Github Project: [Github](https://github.com/andysaurin/3DL_NuCount/)
{"language": ["en"], "license": "gpl-3.0", "pipeline_tag": "image-segmentation"}
transformers image-segmentation en arxiv:1908.03636 doi:10.57967/hf/0418 license:gpl-3.0 endpoints_compatible has_space region:us
TAGS
#transformers #image-segmentation #en #arxiv-1908.03636 #doi-10.57967/hf/0418 #license-gpl-3.0 #endpoints_compatible #has_space #region-us 


# 3DL_NuCount model


__Model author__: Fabrice Daian


#### Model description

__3DL_NuCount__ model has been designed by fine tuning a pretrained Stardist3D model [1,2] using a home made dataset [3] in order to assess the number of cells present in a given 3D image stack acquired using an optical microscope. Training and Inference Notebooks are hosted on our Github repo [4].

#### Stardist Training parameters

- patch size:  (48,96,96)
- batch size:  32
- epochs    :  100
- data augmentation : flip/rotation/intensity
- image normalization: normalize channel independantly
- anisotropy:  empirical
- rays      :  96

#### Training dataset parameters
- tile size : (4,63,128,128)
- split     : Train 0.8 / Val 0.2

#### Inference
- patch size     : (784,784,:)
- image size     : (2048,2048,:)
- model name     : __weights_best_1.h5__
- config file    : __config.json__
- threshold file : __thresholds.json__


#### References
- [1] Stardist Project: Github
- [2] Stardist Paper :  ArXiv
- [3] NuCount Training Dataset  : Zenodo
- [4] NuCount Github Project: Github
# 3DL_NuCount model


__Model author__: Fabrice Daian #### Model description

__3DL_NuCount__ model has been designed by fine tuning a pretrained Stardist3D model [1,2] using a home made dataset [3] in order to assess the number of cells present in a given 3D image stack acquired using an optical microscope. Training and Inference Notebooks are hosted on our Github repo [4]. #### Stardist Training parameters

- patch size:  (48,96,96)
- batch size:  32
- epochs    :  100
- data augmentation : flip/rotation/intensity
- image normalization: normalize channel independantly
- anisotropy:  empirical
- rays      :  96 #### Training dataset parameters
- tile size : (4,63,128,128)
- split     : Train 0.8 / Val 0.2 #### Inference
- patch size     : (784,784,:)
- image size     : (2048,2048,:)
- model name     : __weights_best_1.h5__
- config file    : __config.json__
- threshold file : __thresholds.json__ #### References
- [1] Stardist Project: Github
- [2] Stardist Paper :  ArXiv
- [3] NuCount Training Dataset  : Zenodo
- [4] NuCount Github Project: Github
TAGS
#transformers #image-segmentation #en #arxiv-1908.03636 #doi-10.57967/hf/0418 #license-gpl-3.0 #endpoints_compatible #has_space #region-us 
 # 3DL_NuCount model


__Model author__: Fabrice Daian #### Model description

__3DL_NuCount__ model has been designed by fine tuning a pretrained Stardist3D model [1,2] using a home made dataset [3] in order to assess the number of cells present in a given 3D image stack acquired using an optical microscope. Training and Inference Notebooks are hosted on our Github repo [4]. #### Stardist Training parameters

- patch size:  (48,96,96)
- batch size:  32
- epochs    :  100
- data augmentation : flip/rotation/intensity
- image normalization: normalize channel independantly
- anisotropy:  empirical
- rays      :  96 #### Training dataset parameters
- tile size : (4,63,128,128)
- split     : Train 0.8 / Val 0.2 #### Inference
- patch size     : (784,784,:)
- image size     : (2048,2048,:)
- model name     : __weights_best_1.h5__
- config file    : __config.json__
- threshold file : __thresholds.json__ #### References
- [1] Stardist Project: Github
- [2] Stardist Paper :  ArXiv
- [3] NuCount Training Dataset  : Zenodo
- [4] NuCount Github Project: Github
TAGS
#transformers #image-segmentation #en #arxiv-1908.03636 #doi-10.57967/hf/0418 #license-gpl-3.0 #endpoints_compatible #has_space #region-us 
# 3DL_NuCount model


__Model author__: Fabrice Daian#### Model description

__3DL_NuCount__ model has been designed by fine tuning a pretrained Stardist3D model [1,2] using a home made dataset [3] in order to assess the number of cells present in a given 3D image stack acquired using an optical microscope. Training and Inference Notebooks are hosted on our Github repo [4].#### Stardist Training parameters

- patch size:  (48,96,96)
- batch size:  32
- epochs    :  100
- data augmentation : flip/rotation/intensity
- image normalization: normalize channel independantly
- anisotropy:  empirical
- rays      :  96#### Training dataset parameters
- tile size : (4,63,128,128)
- split     : Train 0.8 / Val 0.2#### Inference
- patch size     : (784,784,:)
- image size     : (2048,2048,:)
- model name     : __weights_best_1.h5__
- config file    : __config.json__
- threshold file : __thresholds.json__#### References
- [1] Stardist Project: Github
- [2] Stardist Paper :  ArXiv
- [3] NuCount Training Dataset  : Zenodo
- [4] NuCount Github Project: Github