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CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images

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CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images

1. Abstract

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.

2. Architecture

2.1 Overall Architecture

CompSegNet Architecture

2.2 Architecture Specifications

CompSegNet Architecture Specifications

2.3 Building Blocks

CompSeg (CSeg) Block

  • 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%.

MBConv vs CSeg

Extended CompSeg (ECSeg) Block

  • 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.

IGC and ECSeg Blocks

Residual Bottleneck Transformer (RBT) Block

  • 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.

RBT Block

Noise-aware stem (Block) Block

  • Stem block
  • Enhances low-level feature extraction while mitigating the impact of noisy features in the model's initial skip connections.

NAS Block

3. Implementation

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.

3.1 Clone the project

For cloning the project run:

   $ git clone https://github.com/mltraore/compsegnet.git
   $ cd compsegnet

3.2 Install requirements

Install requirements using:

   $ chmod +x install_dependencies.sh
   $ pip install -r requirements.txt
   $ sudo ./install_dependencies.sh

3.2 Datasets

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 downloaded here. 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 the prepare_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

3.3 Training

Use the train.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

3.4 Testing

Use the test.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.

4. Results

4.1 Quantitative Results

#FFFFFF#C0C0C0#A0A0A0#808080#505050#000000   CompSegNet on MoNuSeg 2018 dataset

Compsegnet Quantitative results on MoNuSeg 2018 Dataset

#FFFFFF#C0C0C0#A0A0A0#808080#505050#000000   CompSegNet on CoNSeP dataset

Comparison with CoNSeP Competition Results

#FFFFFF#C0C0C0#A0A0A0#808080#505050#000000   CompSegNet on CPM-17 dataset

Comparison with CPM-17 Competition Results

4.2 Qualitative Results

#FFFFFF#C0C0C0#A0A0A0#808080#505050#000000   CompSegNet on MoNuSeg 2018 dataset

Compsegnet Quantitative results on MoNuSeg 2018 Dataset

#FFFFFF#C0C0C0#A0A0A0#808080#505050#000000   CompSegNet on CoNSeP dataset

Compsegnet Quantitative results on MoNuSeg 2018 Dataset

#FFFFFF#C0C0C0#A0A0A0#808080#505050#000000   CompSegNet on CPM-17 dataset

Compsegnet Quantitative results on MoNuSeg 2018 Dataset

Citation

@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}
}

Acknowledgement

This work is supported by TÜBİTAK (Scientific and Technological Research Council of Türkiye) (Project number: 121E379).