Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images.

Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images. Phys Med Biol. 2020 Feb 25;: Authors: Gou S, Tong N, Qi SX, Yang S, Chin RK, Sheng K Abstract Accurate segmentation of organs-at-risk (OARs) is necessary for adaptive head and neck (H&N) cancer treatment planning but manual delineation is tedious, slow, and inconsistent. A Self-Channel-and-Spatial-Attention neural network (SCSA-Net) is developed for H&N OARs segmentation on CT images. To simultaneously ease the training and improve the segmentation performance, the proposed SCSA-Net utilizes the self-attention ability of the network. Spatial and channel-wise attention learning mechanisms are both employed to adaptively force the network to emphasize on the meaningful features and weaken the irrelevant features simultaneously. The proposed network was first evaluated on a public dataset, which includes 48 patients, then on a separate serial CT dataset, which contains ten patients who received weekly diagnostic fan-beam CT scans. On the second dataset, the accuracy of using SCSA-Net to track the parotid and submandibular gland volume changes during radiotherapy treatment was quantified. Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average surface distance (ASD), and 95%maximum surface distance (95SD) were calculated on the brainstem, optic chiasm, optic nerves, mandible, ...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research