Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique.
J Vis Exp
; (209)2024 Jul 05.
Article
em En
| MEDLINE
| ID: mdl-39037268
ABSTRACT
Abdominal multi-organ segmentation is one of the most important topics in the field of medical image analysis, and it plays an important role in supporting clinical workflows such as disease diagnosis and treatment planning. In this study, an efficient multi-organ segmentation method called Swin-PSAxialNet based on the nnU-Net architecture is proposed. It was designed specifically for the precise segmentation of 11 abdominal organs in CT images. The proposed network has made the following improvements compared to nnU-Net. Firstly, Space-to-depth (SPD) modules and parameter-shared axial attention (PSAA) feature extraction blocks were introduced, enhancing the capability of 3D image feature extraction. Secondly, a multi-scale image fusion approach was employed to capture detailed information and spatial features, improving the capability of extracting subtle features and edge features. Lastly, a parameter-sharing method was introduced to reduce the model's computational cost and training speed. The proposed network achieves an average Dice coefficient of 0.93342 for the segmentation task involving 11 organs. Experimental results indicate the notable superiority of Swin-PSAxialNet over previous mainstream segmentation methods. The method shows excellent accuracy and low computational costs in segmenting major abdominal organs.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tomografia Computadorizada por Raios X
Limite:
Humans
Idioma:
En
Revista:
J Vis Exp
Ano de publicação:
2024
Tipo de documento:
Article
País de publicação:
EEUU
/
ESTADOS UNIDOS
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ESTADOS UNIDOS DA AMERICA
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EUA
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UNITED STATES
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UNITED STATES OF AMERICA
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US
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USA