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

Ange Lou; Shuyue Guan; Murray H. Loew

doi:10.1117/1.JMI.10.1.014005

18 February 2023 CaraNet: context axial reverse attention network for segmentation of small medical objects

Ange Lou, Shuyue Guan, Murray H. Loew

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 1, 014005 (February 2023). https://doi.org/10.1117/1.JMI.10.1.014005

Abstract

Purpose

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 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.

Approach

We 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.

Results

We 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.

Conclusions

We proposed CaraNet to segment small medical objects and outperform state-of-the-art methods.

Citation Download Citation

Ange Lou, Shuyue Guan, and Murray H. Loew "CaraNet: context axial reverse attention network for segmentation of small medical objects," Journal of Medical Imaging 10(1), 014005 (18 February 2023). https://doi.org/10.1117/1.JMI.10.1.014005

Received: 20 July 2022; Accepted: 30 January 2023; Published: 18 February 2023

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