This project hosts the code for implementing simultaneous Lymphocyte Detection and Segmentation: Paper

Xiaoxuan Zhang, Xiongfeng Zhu, Kai Tang, Yinghua Zhao, Zixiao Lu, Qianjin Feng,
DDTNet: A dense dual-task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer,
Medical Image Analysis, Volume 78, 2022,

Implementation based on FCOS.

This DDTNet implementation is based on FCOS and maskrcnn-benchmark. Therefore the installation is the same as original FCOS and maskrcnn-benchmark.

Please check INSTALL.md for installation instructions. You may also want to see the original README.md of FCOS and README.md of maskrcnn-benchmark.

• Download BCa-lym dataset (H&E images and original manually annotated dots) from www.andrewjanowczyk.com/use-case-4-lymphocyte-detection/

• Download Post-NAT-BRCA dataset (H&E images and original manually annotated dots) from here

• If you want to use your own dataset, the data should be prepared in the format:

project
│   README.md  
└───datasets
    └───lym
        ├──Images
        │  ├──001.png
        │  ├──002.png
        │  └...
        ├──ImageSets
        │  ├──Dataset Name1
        │  │  ├──train1.txt
        │  │  ├──test1.txt
        │  │  ├──train2.txt
        │  │  ├──test2.txt
        │  │  └...
        │  ├──Dataset Name2
        │  │  └...
        │  └...
        └──Masks
           ├──mask_001.png
           ├──edgemask_001.png
           ├──Dis_001.png       
           ├──mask_002.png
           ├──edgemask_002.png
           ├──Dis_002.png
           └...

Note:

1. Images should be H&E images (RGB images). If it is not *.png, modify lym.py.
2. *.txt should include the names of all data in the training set or test set, for example, '001', '002', '003'...
3. mask*.png, edgemask*.png and Dis*.png are mask map, contour map and distance map corresponding to input image, respectively.

The following command line will train ddtnet_R_101_FPN_1x on 1 GPUs with Synchronous Stochastic Gradient Descent (SGD):

python -m torch.distributed.launch \
    --nproc_per_node=1 \
    --master_port=$((RANDOM + 10000)) \
    tools/train_net.py \
    --config-file configs/ddtnet/ddtnet_R_101_FPN_1x.yaml \
    DATALOADER.NUM_WORKERS 2 \

The inference command line:

python tools/eval_net.py \
    --config-file configs/ddtnet/ddtnet_R_101_FPN_1x.yaml \
    MODEL.WEIGHT model_final.pth \
    TEST.IMS_PER_BATCH 4    

The Inference results include a list of bounding box coordinates, a mask map and a contour map for each input image. Use Alignment.m to unify the detection and segmentation results.

We would like to thank FCOS for overall framework.