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CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images
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In histopathology, nuclei within images hold vital diagnostic information. Automated segmentation of nuclei can alleviate pathologists' workload and enhance diagnostic accuracy. Although U-Net-based methods are prevalent, they face challenges like overfitting and limited field-of-view. This paper introduces a new U-shaped architecture (CompSegNet) for nuclei segmentation in H&E histopathology images by developing enhanced convolutional blocks and a Residual Bottleneck Transformer (RBT) block. The proposed convolutional blocks are designed by enhancing the Mobile Convolution (MBConv) block through a receptive fields enlargement strategy, which we referred to as the Zoom-Filter-Rescale (ZFR) strategy and a global context modeling based on the global context (GC) Block; and the proposed RBT block is developed by incorporating the Transformer encoder blocks in a tailored manner to a variant of the Sandglass block. Additionally, a noise-aware stem block and a weighted joint loss function are designed to improve the overall segmentation performance. The proposed CompSegNet outperforms existing methods quantitatively and qualitatively, achieving a competitive AJI score of 0.705 on the MoNuSeg 2018 dataset, 0.72 on the CoNSeP dataset, and 0.779 on the CPM-17 dataset while maintaining a reasonable parameter count.
- Enhances MBConv efficiency through the Zoom-Filter-Rescale (ZFR) strategy.
- Involves:
- Large stride at the entry expansion layer (Zooming).
- Large kernel-sized depthwise convolution operation (Zooming and Filtering).
- Rescaling spatial size using bilinear upsampling to compensate for downsampling (Rescaling).
- Increases receptive fields within MBConv by 8×.
- Decreases inference latency by approximately 30%.
- Extends the CSeg block by incorporating an improved global context (IGC) block for robust global context modeling.
- Increases AJI score of the nuclei segmentation on MoNuSeg 2018 dataset in a lightweight network by approximately 1.9%.
- Achieves improved performance with only a minimal computational cost.
- Incorporates Transformer encoder blocks in a tailored manner to a variant of the Sandglass block.
- Leverages strengths of both convolutional and attention-based mechanisms.
- Effectively captures fine-grained spatial information and comprehensive long-range dependencies.
- Stem block
- Enhances low-level feature extraction while mitigating the impact of noisy features in the model's initial skip connections.
The proposed architecture is implemented using the Keras API with a TensorFlow 2.12 backend, running on a Google Colab Pro+ instance equipped with an Intel(R) Xeon(R) CPU and an NVIDIA A100-SXM4-40GB GPU.
For cloning the project run:
$ git clone https://github.com/mltraore/compsegnet.git $cd compsegnetInstall requirements using:
$ chmod +x install_dependencies.sh $ pip install -r requirements.txt $ sudo ./install_dependencies.sh
The following datasets were used in experiments:
Processed versions of these datasets, along with the original versions, predicted masks, and a checkpoint sample for the MoNuSeg 2018 dataset, can be downloadedhere. For automatic download, run:
$ chmod +x download_resources.sh $ ./download_resources.sh
in the project directory.
- Note: The included predicted masks are actual CompSegNet architecture outputs, provided for qualitative comparison and further evaluation by the authors in their research.
To create the datasets yourself, use theprepare_dataset.py script:
# Get script usage info$ python3 prepare_dataset.py --help# Example usage$ python3 prepare_dataset.py --dataset-path datasets/monuseg_2018 \ --image-size 1000 \ --validation-size 0.10 \ --reference-image datasets/monuseg_2018/train/images/1.tif \ --prepared-data-path datasets/prepared_datasets/monuseg_2018
Use thetrain.py script in the project directory to train the model:
# Get script usage info$ python3 train.py --help# Example usage$ python3 train.py --train-folder datasets/prepared_datasets/monuseg_2018/train \ --validation-folder datasets/prepared_datasets/monuseg_2018/validation \ --checkpoints-folder checkpoints/ckpts
Use thetest.py script in the project directory to test the model:
# Get script usage info$ python3 test.py --help# Example usage$ python3 test.py --model-weights-save checkpoints/ckpts \ --test-set datasets/prepared_datasets/monuseg_2018/train
This provides Dice Similarity Coefficient (DSC), Aggregated Jaccard Index (AJI), and Hausdorff Distance (HD) scores.
CompSegNet on MoNuSeg 2018 dataset
CompSegNet on CoNSeP dataset
CompSegNet on CPM-17 dataset
CompSegNet on MoNuSeg 2018 dataset
CompSegNet on CoNSeP dataset
CompSegNet on CPM-17 dataset
@article{traore2024compsegnet, title={CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H\&E histopathology images}, author={Traoré, Mohamed and Hancer, Emrah and Samet, Refik and Yildirim, Zeynep and Nemati, Nooshin}, journal={Biomedical Signal Processing and Control}, volume={97}, pages={106699}, year={2024}, publisher={Elsevier}}This work is supported by TÜBİTAK (Scientific and Technological Research Council of Türkiye) (Project number: 121E379).