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...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE access Vol. 11; pp. 101630 - 101643
Main Authors:	Ren, Chengjuan, Guo, Ziyu, Ren, Huipeng, Jeong, Dongwon, Kim, Dae-Kyoo, Zhang, Shiyan, Wang, Jiacheng, Zhang, Guangnan
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	ASPP Clinical diagnosis Coders Datasets Decoding Diagnose diseases Encoding feature fusion Image segmentation Lesions Magnetic resonance imaging multi-encoder and decoder Principal component analysis Prostate Prostate cancer segment prostate Sensitivity Transformers
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!