Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task

Purpose To create an automated segmentation method for the sellar region, several tools to extract invasiveness-related features, and evaluate their clinical usefulness by predicting the tumor consistency. Materials and Methods Patients included were diagnosed with pituitary adenoma at Peking Union Medical College Hospital. A deep convolutional neural network, called Gated-Shaped U-Net (GSU-Net), was created to automatically segment the sellar region into eight classes. Five MRI features were extracted from the segmentation results, including tumor diameters, volume, optic chiasma height, Knosp grading system, and degree of ICA contact. The clinical usefulness of proposed methods was evaluated by the diagnostic accuracy of the tumor consistency. Results 163 patients confirmed with pituitary adenoma were included as the first group and were randomly divided into a training dataset and test dataset (131 and 32 patients, respectively). 50 patients confirmed with acromegaly were included as the second group. The Dice coefficient of pituitary adenoma in important image slices was 0.940. The proposed methods achieved accuracies of over 80% for the prediction of five invasive-related MRI features. Methods derived from the automatic segmentation showing better performances than original methods and achieved AUCs of 0.840 and 0.920 for clinical models and radiomics models, respectively. Conclusion The proposed methods could automatically segment the sellar region and extract features with high accuracies. The outstanding performance of the prediction of the tumor consistency indicates their clinical usefulness for supporting neurosurgeons in judging patients’ conditions, predicting prognosis, and other downstream tasks during the preoperative period.