ARCSeg - Supervised Semantic Segmentation Benchmarking Framework for Surgical Robotics 📹 🤖 🪡

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The goal of ARCSeg is to produce accurate segmentation masks for images from laparascopic gallbladder removal surgeries (cholecystectomy)

To achieve this goal, we have conducted an extensive study of various semantic segmentation networks including:

• UNet
• UNet++
• DeepLabV3+
• MANet

For each of these networks, experiments are split between:

• Initializing the networks from scratch
• Initializing the networks from pretrained weights

When initializing the networks, we have found success in using the following encoders with pretrained weights:

• ResNet18
• ResNet34
• ResNet50
• EfficientNet-B4

The above ResNets and EfficientNet-B4 are all trained on ImageNet, and they serve as strong encoder backbones for our segmentation networks

Training a Semantic Segmentation Network

To run a Semantic Segmentation experiment, you can run the following commands:

cd scripts
./trainSegNet.sh

The data for the experiments are organized as follows:

|__ src
	|__ data
		|__ datasets
                    |__ synapse
                        |__ train
                            |__ images
                            |__ groundtruth
                        |__ trainval
                            |__ images
                            |__ groundtruth
                        |__ test
                            |__ images
                            |__ groundtruth
                            
                    |__ cholec
                        |__ train
                            |__ images
                            |__ groundtruth
                        |__ trainval
                            |__ images
                            |__ groundtruth
                        |__ test
                            |__ images
                            |__ groundtruth

Here, the synapse dataset is provided to us by the hosts of the HeiChole 2021 competition, and cholec is provided to us from the following Kaggle data repository: https://www.kaggle.com/newslab/cholecseg8k. Currently, we use cholec as a pretraining dataset.

Prior to running your experiments, though, be aware of the following models:

model="smp_UNet++" 
# other acceptable options include:
# model="segnet"
# model="unet"
# model="resnet18_unet"
# model="smp_unet18"
# model="smp_deeplabv3+"
# model="smp_MANet"

If the model contains the smp prefix, this means the model comes from the Segmentation Models in PyTorch package. Further, each smp model is initialized with a resnet18 encoder trained on ImageNet by default. To implement your own parameters and tune the models, please look at the following technical documentation

Otherwise, unet is trained from scratch, and segnet is adopted from the following paper: m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks. Additionally, our code repository is based on this paper's repository, which can be found here.

Our repository has introduced many updates to the m2caiSeg paper's repository, and additionally, we have expanded the code to work with various models, as can be seen above.

If you have any questions, please file an issue, and we will address your inquiry as best and as soon as we can.

Environment Setup

• Conda Environment Configuration
  • Using conda environment to containerize PyTorch and CUDA dependencies for ARCSeg experiments (assuming you have installed Miniconda or Anaconda)
  • Environment Creation Commands:

    conda create --name torch python=3.7
    conda activate torch
    conda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch
    conda install numpy matplotlib scikit-learn pandas tqdm jupyter scikit-image
    conda install -c conda-forge tensorboardx
    conda install -c conda-forge opencv
    conda install -c anaconda scipy=1.5.3
    

  • Note: With the above torch environment, all experiments were successfully run on Ubuntu 21.04 with NVIDIA GeForce RTX 3080 Mobile / Max-Q 16GB VRAM GPU and CUDA Version: 11.3 [Checked via nvidia-smi]