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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/18717
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| タイトル: | Two-Path Augmented Directional Context Aware Ultrasound Image Segmentation |
| 著者: | Song, Yuhan
Elibol, Armagan
Chong, Nak Young |
| キーワード: | Artificial Intelligence
Medical Image Segmentation
Robotic Ultrasonography |
| 発行日: | 2023-08-22 |
| 出版者: | Institute of Electrical and Electronics Engineers (IEEE) |
| 誌名: | 2023 IEEE International Conference on Mechatronics and Automation |
| DOI: | 10.1109/ICMA57826.2023.10215672 |
| 抄録: | The segmentation of ultrasound (US) images plays a crucial role in the development of end-to-end smart diagnosis systems. In the diagnostic stage, specific diagnosis programs can be applied to well-cropped sub-regions within a single US image, catering to different medical interests. In this study, we propose and test a neural network model designed to perform end-toend segmentation on abdominal US images, with a focus on five different anatomical structures: liver, kidney, vessels, gallbladder, and spleen. The main contribution of our work lies in the exploration of multi-organ/tissue segmentation. Unlike previous research, our approach takes into account two inherent features of US images: (1) significant variations in spatial sizes among different organs and tissues, and (2) the relatively consistent spatial relationships among anatomical structures within the human body.
To address these considerations, we introduce a novel image segmentation model that combines the feature pyramid network (FPN) and the spatial recurrent neural network (SRNN). In our paper, we describe the utilization of FPN for extracting anatomical structures of varying scales, as well as the implementation of SRNN to capture spatial context features within abdominal US images. Our model incorporates both top-down and bottom-up pathways, enhancing both semantic features and spatial context features. We refer to this as the ”two-path augmented” approach. Furthermore, we incorporate a directional attention mechanism, which selectively leverages spatial context information from four principal directions. This is the essence of our ”directional context aware” component. The performance of our proposed model is evaluated through both quantitative and qualitative measures. The evaluation results demonstrate the competitiveness of our approach, and the inclusion of spatial contextual information has resulted in improved performance compared to using the pure feature pyramid network alone. |
| Rights: | This is the author's version of the work. Copyright (C) 2023 IEEE. 2023 IEEE International Conference on Mechatronics and Automation (ICMA), 2023. DOI:10.1109/ICMA57826.2023.10215672. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| URI: | http://hdl.handle.net/10119/18717 |
| 資料タイプ: | author |
| 出現コレクション: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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| N-CHONG-I-0904-2.pdf | | 4951Kb | Adobe PDF | 見る/開く |
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