Cross-layer connected network with adaptive attention mechanism for 3D multi-organ and tumor segmentations from CT

Abstract

3D medical image segmentation is one of the most important and challenging tasks for medical image analysis. To enhance the feature extraction ability of 3D convolutional neural networks (CNN) and improve the segmentation accuracy on small tumors, this paper presents a cross-layer connected network with an adaptive attention mechanism to achieve efficient segmentations for 3D multi-organ and tumor from CT. A cross layer connection is proposed to utilize features more effectively by reusing multiple layers of semantic information while recovering spatial information. The pre-activation Residual Block and Squeeze-and-Excitation (SE) block are introduced to enhance the feature extraction ability. Due to the serious class imbalance problem caused by small tumors, the training process of the 3D CNN model is unstable and small targets are difficult to segment. To relieve the training instability problem, Anti-Aliased Pooling (AAP) is introduced to replace the original maxpooling. Moreover, a region of interest (ROI) adaptive attention mechanism based on the auxiliary loss function is developed to improve the segmentation accuracy on small target tumors by combining the target detection method with semantic segmentation models. Extensive experiments show that the dice coefficient of 3D liver tumor segmentation on the LiTS dataset reaches 0.668 by using our designed network, and the experiments on the BRATS dataset for 3D brain tumor segmentation yield average dice coefficient, sensitivity, and Hausdorff distance of 0.706, 0.730, and 5.972 mm, respectively, which are obviously superior to some existing methods.