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Deep learning-based medical image segmentation technology aims at automaticrecognizing and annotating objects on the medical image. Non-local attentionand feature learning by multi-scale methods are widely used to model network,which drives progress in medical image segmentation. However, those attentionmechanism methods have weakly non-local receptive fields' strengthenedconnection for small objects in medical images. Then, the features of importantsmall objects in abstract or coarse feature maps may be deserted, which leadsto unsatisfactory performance. Moreover, the existing multi-scale methods onlysimply focus on different sizes of view, whose sparse multi-scale featurescollected are not abundant enough for small objects segmentation. In this work,a multi-dimensional attention segmentation model with cascade multi-scaleconvolution is proposed to predict accurate segmentation for small objects inmedical images. As the weight function, multi-dimensional attention modulesprovide coefficient modification for significant/informative small objectsfeatures. Furthermore, The cascade multi-scale convolution modules in eachskip-connection path are exploited to capture multi-scale features in differentsemantic depth. The proposed method is evaluated on three datasets: KiTS19,Pancreas CT of Decathlon-10, and MICCAI 2018 LiTS Challenge, demonstratingbetter segmentation performances than the state-of-the-art baselines.

w-Net: Dual Supervised Medical Image Segmentation Model with Multi-Dimensional Attention and Cascade Multi-Scale Convolution