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:

• other loss function
• 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.