Image-driven classification of functioning and nonfunctioning pituitary adenoma by deep convolutional neural networks.

ABSTRACT: The secreting function of pituitary adenomas (PAs) plays a critical role in making the treatment strategies. However, Magnetic Resonance Imaging (MRI) analysis for pituitary adenomas is labor intensive and highly variable among radiologists. In this work, by applying convolutional neural network (CNN), we built a segmentation and classification model to help distinguish functioning pituitary adenomas from non-functioning subtypes with 3D MRI images from 185 patients with PAs (two centers). Specifically, the classification model adopts the concept of transfer learning and uses the pre-trained segmentation model to extract deep features from conventional MRI images. As a result, both segmentation and classification models obtained high performance in two internal validation datasets and an e

SUBMITTER: Li H 

PROVIDER: S-EPMC8178077 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature