MEF-UNet : An end-to-end ultrasound image segmentation algorithm based on multi-scale feature extraction and fusion
Copyright © 2024 Elsevier Ltd. All rights reserved..
Ultrasound image segmentation is a challenging task due to the complexity of lesion types, fuzzy boundaries, and low-contrast images along with the presence of noises and artifacts. To address these issues, we propose an end-to-end multi-scale feature extraction and fusion network (MEF-UNet) for the automatic segmentation of ultrasound images. Specifically, we first design a selective feature extraction encoder, including detail extraction stage and structure extraction stage, to precisely capture the edge details and overall shape features of the lesions. In order to enhance the representation capacity of contextual information, we develop a context information storage module in the skip-connection section, responsible for integrating information from adjacent two-layer feature maps. In addition, we design a multi-scale feature fusion module in the decoder section to merge feature maps with different scales. Experimental results indicate that our MEF-UNet can significantly improve the segmentation results in both quantitative analysis and visual effects.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:114 |
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| Enthalten in: |
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society - 114(2024) vom: 21. Apr., Seite 102370 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xu, Mengqi [VerfasserIn] |
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| Links: |
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| Themen: |
Convolutional neural network |
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| Anmerkungen: |
Date Completed 01.04.2024 Date Revised 01.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compmedimag.2024.102370 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Ultrasound image segmentation is a challenging task due to the complexity of lesion types, fuzzy boundaries, and low-contrast images along with the presence of noises and artifacts. To address these issues, we propose an end-to-end multi-scale feature extraction and fusion network (MEF-UNet) for the automatic segmentation of ultrasound images. Specifically, we first design a selective feature extraction encoder, including detail extraction stage and structure extraction stage, to precisely capture the edge details and overall shape features of the lesions. In order to enhance the representation capacity of contextual information, we develop a context information storage module in the skip-connection section, responsible for integrating information from adjacent two-layer feature maps. In addition, we design a multi-scale feature fusion module in the decoder section to merge feature maps with different scales. Experimental results indicate that our MEF-UNet can significantly improve the segmentation results in both quantitative analysis and visual effects | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Convolutional neural network | |
| 650 | 4 | |a Feature fusion | |
| 650 | 4 | |a Image segmentation | |
| 650 | 4 | |a Ultrasound images | |
| 700 | 1 | |a Ma, Qianting |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Huajie |e verfasserin |4 aut | |
| 700 | 1 | |a Kong, Dexing |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Tieyong |e verfasserin |4 aut | |
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