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CaraNet: context axial reverse attention network for segmentation of small medical objects

CaraNet: context axial reverse attention network for segmentation of small medical objects Abstract.PurposeSegmenting 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 have been designed for segmentation tasks and have achieved great success. Few studies, however, have fully considered the sizes of objects; thus, most demonstrate poor performance for small object segmentation. This can have a significant impact on the early detection of diseases.ApproachWe propose a context axial reverse attention network (CaraNet) to improve the segmentation performance on small objects compared with several recent state-of-the-art models. CaraNet applies axial reserve attention and channel-wise feature pyramid modules to dig the feature information of small medical objects. We evaluate our model by six different measurement metrics.ResultsWe test our CaraNet on segmentation datasets for brain tumor (BraTS 2018) and polyp (Kvasir-SEG, CVC-ColonDB, CVC-ClinicDB, CVC-300, and ETIS-LaribPolypDB). 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.ConclusionsWe proposed CaraNet to segment small medical objects and outperform state-of-the-art methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Imaging SPIE

CaraNet: context axial reverse attention network for segmentation of small medical objects

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References (55)

Publisher
SPIE
Copyright
© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)
ISSN
2329-4302
eISSN
2329-4310
DOI
10.1117/1.jmi.10.1.014005
Publisher site
See Article on Publisher Site

Abstract

Abstract.PurposeSegmenting 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 have been designed for segmentation tasks and have achieved great success. Few studies, however, have fully considered the sizes of objects; thus, most demonstrate poor performance for small object segmentation. This can have a significant impact on the early detection of diseases.ApproachWe propose a context axial reverse attention network (CaraNet) to improve the segmentation performance on small objects compared with several recent state-of-the-art models. CaraNet applies axial reserve attention and channel-wise feature pyramid modules to dig the feature information of small medical objects. We evaluate our model by six different measurement metrics.ResultsWe test our CaraNet on segmentation datasets for brain tumor (BraTS 2018) and polyp (Kvasir-SEG, CVC-ColonDB, CVC-ClinicDB, CVC-300, and ETIS-LaribPolypDB). 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.ConclusionsWe proposed CaraNet to segment small medical objects and outperform state-of-the-art methods.

Journal

Journal of Medical ImagingSPIE

Published: Jan 1, 2023

Keywords: small object segmentation; brain tumor; colonoscopy; attention; context axial reverse

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