A multicenter validation study of 3D V-Net-based segmentation model for adrenal glands: Cross-protocol generalization from abdominal CT to chest CT.

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Abstract

To establish a 3D V-Net-based segmentation model for adrenal glands on abdominal CT images and validate its performance in multicenter datasets, including chest CT images. CT images of adrenal glands were retrospectively collected for the training of the adrenal segmentation model. Abdominal CT scans with normal and abnormal adrenal glands (N = 5660) were recruited as the model development cohort and were split into training, internal validation, and internal test sets for the development of the segmentation model. Two groups of health screening subjects were included for model validation: one from the same institution (N = 6126, validation cohort 1) and one from an outside institution (N = 931, validation cohort 2). Their chest CT images were used for model validation. The Dice similarity coefficient (DSC) was used to evaluate the efficacy of the model. The DSC of the test set for left and right adrenal segmentation were 0.920 (0.890-0.930) and 0.910 (0.890-0.930), respectively. In the validation cohorts, the DSC values were 0.816 (0.744-0.866) for the left adrenal gland and 0.819 (0.743-0.865) for the right adrenal gland in validation cohort 1, and 0.752 (0.666-0.820) for the left adrenal gland and 0.747 (0.673-0.812) for the right adrenal gland in validation cohort 2. The 3D V-Net-based adrenal segmentation model achieves considerable segmentation efficacy and demonstrates generalizability from abdominal CT to chest CT, making it suitable for use in CT images with various scanning protocols. The study developed a deep learning model using 3D V-Net for the segmentation of adrenal glands on CT images, achieving good performance of normal and abnormal glands in validation cohorts with different scanning protocols and from multiple institutions, demonstrating its potential as a "flagging" system aiding diagnosis.

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