CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects

TL;DR

CaraNet is a novel neural network designed to enhance the segmentation of small medical objects in images, significantly improving accuracy over existing models by focusing on object size variations.

Contribution

The paper introduces CaraNet, which employs axial reverse attention and feature pyramid modules to better capture small object features in medical image segmentation.

Findings

Achieves top-rank mean Dice accuracy on multiple datasets

Shows significant improvement in small object segmentation

Outperforms several recent state-of-the-art models

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 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 (ARA) and channel-wise feature pyramid (CFP) module to dig feature information of small medical object. And we evaluate our model by six different measurement metrics. 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.