Dual knowledge‐guided two‐stage model for precise small organ segmentation in abdominal CT images
Abstract Multi‐organ segmentation from abdominal CT scans is crucial for various medical examinations and diagnoses. Despite the remarkable achievements of existing deep‐learning‐based methods, accurately segmenting small organs remains challenging due to their small size and low contrast. This arti...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-11-01
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| Series: | IET Image Processing |
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| Online Access: | https://doi.org/10.1049/ipr2.13221 |
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| _version_ | 1850191371325931520 |
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| author | Tao Liu Xukun Zhang Zhongwei Yang Minghao Han Haopeng Kuang Shuwei Ma Le Wang Xiaoying Wang Lihua Zhang |
| author_facet | Tao Liu Xukun Zhang Zhongwei Yang Minghao Han Haopeng Kuang Shuwei Ma Le Wang Xiaoying Wang Lihua Zhang |
| author_sort | Tao Liu |
| collection | DOAJ |
| description | Abstract Multi‐organ segmentation from abdominal CT scans is crucial for various medical examinations and diagnoses. Despite the remarkable achievements of existing deep‐learning‐based methods, accurately segmenting small organs remains challenging due to their small size and low contrast. This article introduces a novel knowledge‐guided cascaded framework that utilizes two types of knowledge—image intrinsic (anatomy) and clinical expertise (radiology)—to improve the segmentation accuracy of small abdominal organs. Specifically, based on the anatomical similarities in abdominal CT scans, the approach employs entropy‐based registration techniques to map high‐quality segmentation results onto inaccurate results from the first stage, thereby guiding precise localization of small organs. Additionally, inspired by the practice of annotating images from multiple perspectives by radiologists, novel Multi‐View Fusion Convolution (MVFC) operator is developed, which can extract and adaptively fuse features from various directions of CT images to refine segmentation of small organs effectively. Simultaneously, the MVFC operator offers a seamless alternative to conventional convolutions within diverse model architectures. Extensive experiments on the Abdominal Multi‐Organ Segmentation (AMOS) dataset demonstrate the superiority of the method, setting a new benchmark in the segmentation of small organs. |
| format | Article |
| id | doaj-art-a5acf9ebfba24090837fb882fceae63a |
| institution | OA Journals |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-a5acf9ebfba24090837fb882fceae63a2025-08-20T02:14:56ZengWileyIET Image Processing1751-96591751-96672024-11-0118133935394910.1049/ipr2.13221Dual knowledge‐guided two‐stage model for precise small organ segmentation in abdominal CT imagesTao Liu0Xukun Zhang1Zhongwei Yang2Minghao Han3Haopeng Kuang4Shuwei Ma5Le Wang6Xiaoying Wang7Lihua Zhang8Academy for Engineering and Technology Fudan University Shanghai ChinaAcademy for Engineering and Technology Fudan University Shanghai ChinaAcademy for Engineering and Technology Fudan University Shanghai ChinaAcademy for Engineering and Technology Fudan University Shanghai ChinaAcademy for Engineering and Technology Fudan University Shanghai ChinaAcademy for Engineering and Technology Fudan University Shanghai ChinaAcademy for Engineering and Technology Fudan University Shanghai ChinaZhongshan Hospital Fudan University Shanghai ChinaAcademy for Engineering and Technology Fudan University Shanghai ChinaAbstract Multi‐organ segmentation from abdominal CT scans is crucial for various medical examinations and diagnoses. Despite the remarkable achievements of existing deep‐learning‐based methods, accurately segmenting small organs remains challenging due to their small size and low contrast. This article introduces a novel knowledge‐guided cascaded framework that utilizes two types of knowledge—image intrinsic (anatomy) and clinical expertise (radiology)—to improve the segmentation accuracy of small abdominal organs. Specifically, based on the anatomical similarities in abdominal CT scans, the approach employs entropy‐based registration techniques to map high‐quality segmentation results onto inaccurate results from the first stage, thereby guiding precise localization of small organs. Additionally, inspired by the practice of annotating images from multiple perspectives by radiologists, novel Multi‐View Fusion Convolution (MVFC) operator is developed, which can extract and adaptively fuse features from various directions of CT images to refine segmentation of small organs effectively. Simultaneously, the MVFC operator offers a seamless alternative to conventional convolutions within diverse model architectures. Extensive experiments on the Abdominal Multi‐Organ Segmentation (AMOS) dataset demonstrate the superiority of the method, setting a new benchmark in the segmentation of small organs.https://doi.org/10.1049/ipr2.13221computerised tomographylearning (artificial intelligence)medical image processing |
| spellingShingle | Tao Liu Xukun Zhang Zhongwei Yang Minghao Han Haopeng Kuang Shuwei Ma Le Wang Xiaoying Wang Lihua Zhang Dual knowledge‐guided two‐stage model for precise small organ segmentation in abdominal CT images IET Image Processing computerised tomography learning (artificial intelligence) medical image processing |
| title | Dual knowledge‐guided two‐stage model for precise small organ segmentation in abdominal CT images |
| title_full | Dual knowledge‐guided two‐stage model for precise small organ segmentation in abdominal CT images |
| title_fullStr | Dual knowledge‐guided two‐stage model for precise small organ segmentation in abdominal CT images |
| title_full_unstemmed | Dual knowledge‐guided two‐stage model for precise small organ segmentation in abdominal CT images |
| title_short | Dual knowledge‐guided two‐stage model for precise small organ segmentation in abdominal CT images |
| title_sort | dual knowledge guided two stage model for precise small organ segmentation in abdominal ct images |
| topic | computerised tomography learning (artificial intelligence) medical image processing |
| url | https://doi.org/10.1049/ipr2.13221 |
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