|
JAIST Repository >
School of Information Science >
Conference Papers >
Conference Papers >
Please use this identifier to cite or link to this item:
https://hdl.handle.net/10119/20313
|
| Title: | Abdominal Multi-Organ Segmentation Using Multi-Scale and Context-Aware Neural Networks |
| Authors: | Song, Yuhan
Elibol, Armagan
Chong, Nak Young |
| Keywords: | Medical Imaging and Processing
Diagnostic Ultrasound
Image Segmentation
Feature Pyramid Network
Trident Network |
| Issue Date: | 2024-02-27 |
| Publisher: | Elsevier |
| Magazine name: | IFAC Journal of Systems and Control |
| Volume: | 27 |
| Start page: | 100249 |
| DOI: | 10.1016/j.ifacsc.2024.100249 |
| Abstract: | Recent advancements in AI have significantly enhanced smart diagnostic methods, bringing us closer to achieving end-to-end diagnosis. Ultrasound image segmentation plays a crucial role in this diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we consider two inherent features of ultrasound images: (1) different organs and tissues vary in spatial sizes, and (2) the anatomical structures inside the human body form a relatively constant spatial relationship. Based on those two ideas, we proposed two segmentation models combining multiscale convolution neural network backbones and a spatial context feature extractor. We discuss two backbone structures to extract anatomical structures of different scales: the Feature Pyramid Network(FPN) backbone and the Trident Network backbone. Moreover, we show how Spatial Recurrent Neural Network(SRNN) is implemented to extract the spatial context features in abdominal ultrasound images. Our proposed model has achieved dice coefficient score of 0.919 and 0.931, respectively. |
| Rights: | Copyright (C) 2024, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license (CC BY-NC-ND 4.0). [http://creativecommons.org/licenses/by-nc-nd/4.0/] NOTICE: This is the author's version of a work accepted for publication by Elsevier. Yuhan Song, Armagan Elibol, Nak Young Chong, IFAC Journal of Systems and Control, Volume 27, March 2024, 100249, https://doi.org/10.1016/j.ifacsc.2024.100249 |
| URI: | https://hdl.handle.net/10119/20313 |
| Material Type: | author |
| Appears in Collections: | b11-1. 会議発表論文・発表資料 (Conference Papers)
|
Files in This Item:
| File |
Description |
Size | Format |
|---|
| N-CHONG-I-0228.pdf | | 5324Kb | Adobe PDF | View/Open |
|
All items in DSpace are protected by copyright, with all rights reserved.
|
