Code - Debesh Jha

Medical Image Segmentation Architectures

PVTFormer

[CT Liver Segmentation Via PVT-based Encoding and Refined Decoding] (ISBI 2024)]

PVTFormer is highly effective for healthy liver segmentation, with potential applications in other medical imaging areas. It represents a significant advancement in medical image segmentation, offering a robust solution for accurate diagnosis and treatment planning.

Github: https://github.com/DebeshJha/PVTFormer

Publication: https://arxiv.org/pdf/2401.09630

MDNet [MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation] [2024]

[MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation] [2024]

A MiT-B2 encoder extracts feature maps at four different levels (F1, F2, F3, and F4) from the input image. Each encoder network is linked to a specific part of the decoder through a multi-scale feature enhancement dilated block, increasing the network depth to generate three distinct segmentation masks. Additionally, the decoders are interconnected so that the output features from preceding decoders are utilized in the subsequent ones to refine the segmentation. Furthermore, the predicted masks from earlier decoders are incorporated into later stages to enhance feature map refinement. This approach ensures spatial attention across foreground and background regions, ultimately improving the final segmentation results.

Github: https://github.com/DebeshJha/MDNet

Publication: https://arxiv.org/pdf/2405.06166

TransNetR

[TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing (MIDL 2023)]

TransNetR is an encoder decoder network which can be used for efficient biomedical image segmentation for both in-distribution and out-of-distribution datasets

Github: https://github.com/DebeshJha/TransNetR

Publication: https://arxiv.org/pdf/2303.07428

TransRUPNet

[TransRUPNet for Improved Out-of-Distribution Generalization in Polyp Segmentation]

We propose TransRUPNet, a real-time deep learning model using Transformers and residual upsampling for colorectal polyp segmentation. It features an encoder-decoder structure with upsampling blocks, achieving 47.07 FPS and a 0.7786 Dice score. Tested on OOD datasets, it outperforms existing methods, ensuring high accuracy and real-time feedback.

Github: https://github.com/DebeshJha/TransRUPNet

Publication: https://arxiv.org/pdf/2306.02176

DoubleUNet

[DoubleU-Net: A Deep Convolutional NeuralNetwork for Medical Image Segmentation]

DoubleU-Net starts with a VGG19 as encoder sub-network, which is followed by decoder sub-network. In the network, the input image is fed to the modified UNet(UNet1), which generates predicted masks (i.e., output1). We then multiply the input image and the produced masks (i.e., output1), which acts as an input for the second modified U-Net(UNet2) that produces another the generated mask (output2). Finally, we concatenate both the masks (output1 and output2) to get the final predicted mask (output).

Github: https://github.com/DebeshJha/2020-CBMS-DoubleU-Net

Publication: https://arxiv.org/pdf/2006.04868

ResUNet++

[ResUNet++: An Advanced Architecture for MedicalImage Segmentation]

The ResUNet++ architecture is based on the Deep Residual U-Net (ResUNet), which is an architecture that uses the strength of deep residual learning and U-Net. The proposed ResUNet++ architecture takes advantage of the residual blocks, the squeeze and excitation block, ASPP, and the attention block

Github: https://github.com/DebeshJha/ResUNetPlusPlus

Publication: https://arxiv.org/pdf/1911.07067

ResUNet++ + CRF + TTA

[A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation]

This is the extension of our previous version of the ResUNet++. In this paper, we describe how the ResUNet++ architecture can be extended by applying Conditional Random Field (CRF) and Test-Time Augmentation (TTA) to further improve its prediction performance on segmented polyps.

Github: https://github.com/DebeshJha/ResUNetPlusPlus-with-CRF-and-TTA

Publication: https://arxiv.org/pdf/1911.07067

More Information on Medical Image Segmentation Architectures.