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.