  Deep learning-based medical image segmentation technology aims at automatic
recognizing and annotating objects on the medical image. Non-local attention
and feature learning by multi-scale methods are widely used to model network,
which drives progress in medical image segmentation. However, those attention
mechanism methods have weakly non-local receptive fields' strengthened
connection for small objects in medical images. Then, the features of important
small objects in abstract or coarse feature maps may be deserted, which leads
to unsatisfactory performance. Moreover, the existing multi-scale methods only
simply focus on different sizes of view, whose sparse multi-scale features
collected are not abundant enough for small objects segmentation. In this work,
a multi-dimensional attention segmentation model with cascade multi-scale
convolution is proposed to predict accurate segmentation for small objects in
medical images. As the weight function, multi-dimensional attention modules
provide coefficient modification for significant/informative small objects
features. Furthermore, The cascade multi-scale convolution modules in each
skip-connection path are exploited to capture multi-scale features in different
semantic depth. The proposed method is evaluated on three datasets: KiTS19,
Pancreas CT of Decathlon-10, and MICCAI 2018 LiTS Challenge, demonstrating
better segmentation performances than the state-of-the-art baselines.
