Automatic segmentation of colon, small intestine, and duodenum based on scale attention network.

Purpose: Automatic segmentation of colon, small intestine, and duodenum is a challenging task because of the great variability in the scale of the target organs. Multi‐scale features are the key to alleviating this problem. Previous works focused on extracting discriminative multi‐scale features through a hierarchical structure. Instead, the purpose of this work is to exploit these powerful multi‐scale features more efficiently. Methods: A Scale Attention Module (SAM) was proposed to recalibrate multi‐scale features by explicitly modeling their importance score adaptively. The SAM was introduced into the segmentation model to construct the Scale Attention Network (SANet). The multi‐scale features extracted from the encoder were first re‐extracted to obtain more specific multi‐scale features. Then the SAM was applied to recalibrate the features. Specifically, for the feature of each scale, a summation of Global Average Pooling and Global Max Pooling was used to create scale‐wise feature representations. According to the representations, a lightweight network was used to generate the importance score of each scale. The features were recalibrated based on the scores, and a simple pixel‐by‐pixel summation was used to fuse the multi‐scale features. The fused multi‐scale feature was fed into a segmentation head to complete the task. Results: The models were evaluated using fivefold cross‐validation on 70 upper abdominal computed tomography scans of patients in a volume manner. The results showed that SANet could effectively alleviate the scale‐variability problem and achieve better performance compared with UNet, Attention UNet, UNet++, Deeplabv3p, and CascadedUNet. The Dice similarity coefficients (DSCs) of colon, small intestine, and duodenum were (84.06 ± 3.66)%, (76.79 ± 5.12)%, and (61.68 ± 4.32)%, respectively. The HD95 were (7.51 ± 2.45) mm, (11.08 ± 2.45) mm, and (12.21 ± 1.95) mm, respectively. The values of relative volume difference were (3.4 ± 0.8)%, (11.6 ± 11.81)%, and (6.2 ± 3.71)%, respectively. The values of center‐of‐mass distance were 7.85 ± 2.82, 9.89 ± 2.70, and 9.94 ± 1.58, respectively. Compared with other attention modules and multi‐scale feature exploitation approaches, SAM could obtain a 0.83–2.71 points improvement in terms of DSC with a comparable or even less number of parameters. The extensive experiments confirmed the effectiveness of SAM. Conclusions: The SANet can efficiently exploit multi‐scale features to alleviate the scale‐variability problem and improve the segmentation performance on colon, small intestine, and duodenum of the upper abdomen. [ABSTRACT FROM AUTHOR]