https://github.com/assassint2017/abdominal-multi-organ-segmentation

abdominal multi-organ segmentation using pytorch
https://github.com/assassint2017/abdominal-multi-organ-segmentation

multi-organ-segmentation pytorch segmentation

Last synced: 3 months ago
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abdominal multi-organ segmentation using pytorch

• Host: GitHub
• URL: https://github.com/assassint2017/abdominal-multi-organ-segmentation
• Owner: assassint2017
• Created: 2018-07-19T13:13:00.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-27T19:49:58.000Z (over 5 years ago)
• Last Synced: 2024-04-10T21:49:00.727Z (7 months ago)
• Topics: multi-organ-segmentation, pytorch, segmentation
• Language: Python
• Homepage:
• Size: 73.2 MB
• Stars: 127
• Watchers: 7
• Forks: 46
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome_medical - abdominal-multi-organ-segmentation - Atlas Labeling Beyond the Cranial Vault ](https://www.synapse.org/#!Synapse:syn3193805/wiki/217752)|Composed of two U-shape like 3D FCN|| (Segmentation)

README

        
# abdominal-multi-organ-segmentation

abdominal multi-organ segmentation using pytorch,**pytorch version: 0.4.0**

the data come from an online challenge called **Multi-atlas labeling Beyond the Cranial Vault**, for the detail, you can check this link:**https://www.synapse.org/#!Synapse:syn3193805/wiki/217752**. in this challenge, the task is to segement 13 different kind of organ as follow:



## data management

i use the trainging set given by the competition organizer. The training set include 30 CT data.I randomly divided it into 25 for training and 5 for evaluation. and organize them as follow:



## data process

i normalized the axial spacing to 3mm. and truncated the hu value to a certain range. only the slice contain organ are used to train the network.

## network architecture

i use two u-shape like 3D FCN, and add residual connection at a certain group of convlayers. In order to increase the receptive field，i add some hybrid dilated convlayer to the last two stage of the encoder.most idea come form [1].

## implementation detail

i use adam optim and set the initial learning rate to 1e-4, train on three GTX 1080TI with batch size equal to three.the whole trainging process take about 13 hours.

## result

i use mean dice coefficient as metrics.

|strategy|spleen|right kidney|left kidney|gallbladder|esophagus|liver|stomach|aorta|inferior vena cava|portal vein and splenic vein|pancreas|right adrenal gland|left adrenal gland|

|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|

|ava_dice_loss|0.830|0.745|0.712|0.143|0.000|0.880|0.654|0.686|0.605|0.500|0.429|0.089|0.111|

i have implement different kind of loss function, you can try which one work best in your data.

**Here is the best of the above results:**



**you can copy the value in bset_result.xlsx to show.xlsx to get the above picture**

## TODO:

- [X] other loss function

- [X] data augmentation

## references

1. Roth H R, Shen C, Oda H, et al. A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation[J]. arXiv preprint arXiv:1806.02237, 2018.

2. Milletari F, Navab N, Ahmadi S A. V-net: Fully convolutional neural networks for volumetric medical image segmentation[C]//3D Vision (3DV), 2016 Fourth International Conference on. IEEE, 2016: 565-571.

3. Fidon L, Li W, Garcia-Peraza-Herrera L C, et al. Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks[C]//International MICCAI Brainlesion Workshop. Springer, Cham, 2017: 64-76.

4. Sudre C H, Li W, Vercauteren T, et al. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations[M]//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, Cham, 2017: 240-248.