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A U-shaped Network based on Convolution Coupled Transformer for Segmentation of Peripheral and Transition Zones in Prostate MRI

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CCT-Unet

This repo is the official implementation of "A U-shaped Network based on Convolution Coupled Transformer for Segmentation of Peripheral and Transition Zones in Prostate MRI".

overview

In this work, a U-shaped network based on the convolution coupled Transformer is proposed for segmentation of peripheral and transition zones in prostate MRI, named the convolution coupled Transformer U-Net (CCT-Unet). The convolutional embedding block is first designed for encoding high-resolution input to retain the edge detail of the image. Then the convolution coupled Transformer block is proposed to enhance the ability of local feature extraction and capture long-term correlation that encompass anatomical information. The feature conversion module is also proposed to alleviate the semantic gap in the process of jumping connection.

Pretrain model on ProstateX

Model Dataset Resolution #Params Flops Pretrain model
CCT-Unet ProstateX 224x224 7.023 G 27.59 M model

Requirements

einops==0.6.1
numpy==1.23.5
timm==0.6.13
torch==1.12.1

Reference

●A U-shaped Network based on Convolution Coupled Transformer for Segmentation of Peripheral and Transition Zones in Prostate MRI, Yifei Yan, Rongzong Liu, Haobo Chen, Limin Zhang, Qi Zhang
●G. Litjens, O. Debats, J. Barentsz, et al., “Computer-aided detection of prostate cancer in MRI,” IEEE Trans. Med. Imaging, vol. 33, no. 5, pp. 1083–1092, 2014.
●Y. Liu, K. Sung, G. Yang, et al., “Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attention,” IEEE Access, vol. 7, pp. 163626–163632, 2019.

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A U-shaped Network based on Convolution Coupled Transformer for Segmentation of Peripheral and Transition Zones in Prostate MRI

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