Few-shot medical image segmentation using a global correlation network with discriminative embedding (CBM 2021) (Link)

A Pytorch Implementation of ''Few-shot medical image segmentation using a global correlation network with discriminative embedding'', which is accepted by the jounal of Computers in Biology and Medicine.

Requirements

• Python == 3.7.4
• Pytorch == 1.1.0
• Torchvision == 0.3.0
• CUDA 8.0

Train&Test

Training and testing code is being organized and will be released soon.

Results

Quantitative results on MRI measured in DC scores

Organ	Liver	Spleen	Left Kidney	Right Kidney	mean
OSLSM	25.73	34.66	29.21	22.61	28.00
co-FCN	53.74	57.41	60.62	71.13	60.70
PANet	51.37	43.59	25.54	26.45	36.74
SG-One	50.33	42.41	26.79	24.16	35.92
SE-FSS	40.32	48.93	62.56	65.81	54.38
GCN	51.33	58.67	63.67	70.33	61.00
GCN-DE(Ours)	49.47	60.63	76.07	83.03	67.30

Quantitative results on CT measured in DC scores

Organ	Liver	Spleen	Left Kidney	Right Kidney	mean
OSLSM	29.65	19.40	15.82	7.54	18.08
co-FCN	47.50	43.86	41.30	33.51	41.53
PANet	44.25	30.49	25.30	22.95	30.75
SG-One	44.98	30.88	26.79	20.88	30.88
SE-FSS	44.51	40.52	40.10	34.80	39.97
GCN	47.00	46.67	42.33	35.00	42.75
GCN-DE(Ours)	46.77	56.53	68.13	75.50	61.73

Citation

If you find this repository useful, please cite our paper:

@article{sun2022few,
  title={Few-shot medical image segmentation using a global correlation network with discriminative embedding},
  author={Sun, Liyan and Li, Chenxin and Ding, Xinghao and Huang, Yue and Chen, Zhong and Wang, Guisheng and Yu, Yizhou and Paisley, John},
  journal={Computers in biology and medicine},
  volume={140},
  pages={105067},
  year={2022},
  publisher={Elsevier}
}