Abstract
Segmenting medical images accurately and reliably is important for disease diagnosis and treatment. It is a challenging task because of the wide variety of objects’ sizes, shapes, and scanning modalities. Recently, many convolutional neural networks (CNN) have been designed for segmentation tasks and achieved great success. Few studies, however, have fully considered the sizes of objects, and thus most demonstrate poor performance for small objects segmentation. This can have a significant impact on the early detection of diseases. This paper proposes a Context Axial Reserve Attention Network (CaraNet) to improve the segmentation performance on small objects compared with several recent state-of-the-art models. We test our CaraNet on brain tumor (BraTS 2018) and polyp (Kvasir-SEG, CVC-ColonDB, CVC-ClinicDB, CVC-300, and ETIS-LaribPolypDB) segmentation datasets. Our CaraNet achieves the top-rank mean Dice segmentation accuracy, and results show a distinct advantage of CaraNet in the segmentation of small medical objects. Codes available: https://github.com/AngeLouCN/CaraNet
Original language | English |
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Title of host publication | Medical Imaging 2022 |
Subtitle of host publication | Image Processing |
Editors | Olivier Colliot, Ivana Isgum, Bennett A. Landman, Murray H. Loew |
Publisher | SPIE |
ISBN (Electronic) | 9781510649392 |
DOIs | |
Publication status | Published - 2022 |
Event | Medical Imaging 2022: Image Processing - Virtual, Online Duration: 2021 Mar 21 → 2021 Mar 27 |
Publication series
Name | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
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Volume | 12032 |
ISSN (Print) | 1605-7422 |
Conference
Conference | Medical Imaging 2022: Image Processing |
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City | Virtual, Online |
Period | 21/3/21 → 21/3/27 |
Bibliographical note
Publisher Copyright:© 2022 SPIE
Keywords
- Attention
- Brain tumor
- Colonoscopy
- Context axial reverse
- Small object segmentation
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Biomaterials
- Radiology Nuclear Medicine and imaging