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Segmenting medical images accurately and reliably is important for diseasediagnosis and treatment. It is a challenging task because of the wide varietyof objects' sizes, shapes, and scanning modalities. Recently, manyconvolutional neural networks (CNN) have been designed for segmentation tasksand achieved great success. Few studies, however, have fully considered thesizes of objects, and thus most demonstrate poor performance for small objectssegmentation. This can have a significant impact on the early detection ofdiseases. This paper proposes a Context Axial Reserve Attention Network(CaraNet) to improve the segmentation performance on small objects comparedwith 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, andETIS-LaribPolypDB) segmentation datasets. Our CaraNet achieves the top-rankmean Dice segmentation accuracy, and results show a distinct advantage ofCaraNet in the segmentation of small medical objects.

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