VNet
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Segmentation of Kidney and Tumor Competition A Pytorch implementation for <V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation>
3D Segmentation by VNet
Pipeline
Phase1：Coarse Segmentation for Kidney AND Tumor(consider two organs as one, 0: background, 1: Kidney AND Tumor) to get a bounding box of Kidney and Tumor. Post processing such as Maximum connected domain is applied to remove the noise.
Phase2：Detail Segmentation for Kidney OR Tumor(0: background, 1:Kidney, 2:Tumor).Post processing such as Maximum connected domain, changing threshold in sigmod(times a weight for the tumor probability map)
Model
VNet(Phase 1)
Fig from the paper <V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation>
2-step Model(Phase 2)
Can be easily modified to 2-step model.
Dataset
200 CT cases pre-processed by clip operation to range (-250,250) from KiTS19 competition.
Loss Function
Jointly use
- CE loss
- Dice loss
- Focal loss
Reference:
https://blog.csdn.net/m0_37477175/article/details/83004746
Metrics
Dice:
Results of Phase1:
Results of Phase2:
boundbox.py
Get a (384,240,80) bounding box from coarse_bbox.txt(generate from the Phase)
Flaick