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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18791

Title: Abdominal Multi-Organ Segmentation Based on Feature Pyramid Network and Spatial Recurrent Neural Network
Authors: Song, Yuhan
Elibol, Armagan
Chong, Nak Young
Keywords: Medical Imaging and Processing
Diagnostic Ultrasound
Image Segmentation
Feature Pyramid Network
Issue Date: 2023-11-22
Publisher: Elsevier
Magazine name: IFAC-PapersOnLine
Volume: 56
Number: 2
Start page: 3001
End page: 3008
DOI: 10.1016/j.ifacol.2023.10.1426
Abstract: As recent advances in AI are causing the decline of conventional diagnostic methods, the realization of end-to-end diagnosis is fast approaching. Ultrasound image segmentation is an important step in the diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we take two inherent features of ultrasound images into consideration: (1) different organs and tissues vary in spatial sizes, (2) the anatomical structures inside human body form a relatively constant spatial relationship. Based on those two ideas, we propose a new image segmentation model combining Feature Pyramid Network (FPN) and Spatial Recurrent Neural Network (SRNN). We discuss why we use FPN to extract anatomical structures of different scales and how SRNN is implemented to extract the spatial context features in abdominal ultrasound images.
Rights: Copyright (c) 2023 Authors. Yuhan Song, Armagan Elibol, Nak Young Chong. The 22nd World Congress of the International Federation of Automatic Control (IFAC2023), 2023. This is an Open Access article distributed under the terms of Creative Commons Licence CC-BY-NC-ND [https://creativecommons.org/licenses/by-nc-nd/4.0/]. Original publication is available on IFAC-PapersOnLine via https://doi.org/10.1016/j.ifacol.2023.10.1426.
URI: http://hdl.handle.net/10119/18791
Material Type: publisher
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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