Prostate Segmentation in MRI Using Transformer Encoder and Decoder Framework

Prostate Segmentation in MRI Using Transformer Encoder and Decoder Framework

To develop an accurate segmentation model for the prostate and lesion area to help clinicians diagnose diseases, we propose a multi-encoder and decoder segmentation network, denoted Muled-Net, which can concurrently segment the prostate and lesion regions in an image. The model performs parallel cal...

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Bibliographic Details
Main Authors: Chengjuan Ren, Ziyu Guo, Huipeng Ren, Dongwon Jeong, Dae-Kyoo Kim, Shiyan Zhang, Jiacheng Wang, Guangnan Zhang
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Diagnose diseases
segment prostate
multi-encoder and decoder
feature fusion
ASPP
Online Access:https://ieeexplore.ieee.org/document/10244203/

Internet

https://ieeexplore.ieee.org/document/10244203/

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