MSCDA

[MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small Datasets]

Architecture

Overview

Multi-level Semantic-Guided Contrast

Prerequisites

pip install -r requirements.txt

Usages

Train & Testing

We offer several training/testing options as below

• For scenario (--scenario):
  • '1': Scenario 1 (T2W-T1W)
  • '2': Scenario 2 (T1W-T2W)
• For tasks (--task):
  • '4': number of source domain subjects 4
  • '8': number of source domain subjects 8
  • '11': number of source domain subjects 11
• For batchsize (--batchsize, default 32)
• For training/testing epoch (--epoch, default 100)
• For GPU allocation (--gpuid, e.g., '1,2')

example

For MSCDA training:

python train\train_MSCDA.py --scenario 1 --task 4 --batchsize 16 --epoch 200 --gpu 1

For testing model after applying MSCDA:

python test\test_MSCDA.py --scenario 1  --task 4 --batchsize 16 --epoch 200 --gpu 1

Dataset

The datasets are not open access due to the current data-sharing protocal. If you want to run MSCDA based on your own datasets, you can either

(1) reorganize your datasets: Step 1. Resample each image and the corresponding mask to 256*256 and save them in the order of [image, mask] as a NumPy file (.npz). Step 2. Organize files into folders './data/dataset_1' and './data/dataset_2'. Files should be lised as follows:

+-- dataset_1/2
|   +-- DYN/VISTA
|   |   +-- Subject_001
|   |   |   +-- 1.npz
|   |   |   +-- 2.npz
|   |   |   +-- ...
|   |   |
|   |   +-- Subject_002
|   |   |   +-- 1.npz
|   |   |   +-- 2.npz
|   |   |   +-- ...

or

(2) use the core file './uda/MSCDA.py' to fit your own domain adaptation project.

Results

Method performance

Method	Scenario	Task	DSC(%)	JSC(%)	PRC(%)	SEN(%)
Src-Only	1	S11	71.9	58.4	83.1	69.2
Src-Only	1	S8	69.1	56.1	90.9	61.8
Src-Only	1	S4	54.9	41.3	94.1	44.3
Src-Only	2	S11	70.0	58.0	90.5	63.7
Src-Only	2	S8	74.3	65.4	88.5	73.4
Src-Only	2	S4	70.3	57.2	95.7	60.0
MSCDA	1	S11	88.6	79.9	86.5	92.3
MSCDA	1	S8	89.2	81.0	89.3	89.9
MSCDA	1	S4	87.2	78.0	92.4	83.6
MSCDA	2	S11	83.1	71.8	88.7	79.5
MSCDA	2	S8	84.0	73.2	91.7	78.8
MSCDA	2	S4	83.4	72.5	98.0	73.8
Supervised	1	-	95.8	92.8	98.0	94.7
Supervised	2	-	96.0	93.0	96.2	96.5

Segmentation comparison

Citation

@misc{https://doi.org/10.48550/arxiv.2301.02554,
  doi = {10.48550/ARXIV.2301.02554},
  url = {https://arxiv.org/abs/2301.02554},
  author = {Kuang, Sheng and Woodruff, Henry C. and Granzier, Renee and van Nijnatten, Thiemo J. A. and Lobbes, Marc B. I. and Smidt, Marjolein L. and Lambin, Philippe and Mehrkanoon, Siamak},
  keywords = {Quantitative Methods (q-bio.QM), Machine Learning (cs.LG), FOS: Biological sciences, FOS: Biological sciences, FOS: Computer and information sciences, FOS: Computer and information sciences, I.2; I.5},
  title = {MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small Datasets},
  publisher = {arXiv},
  year = {2023},
}