Automated quality check of corpus callosum segmentation using deep learning

Author(s)

Herrera, William Javier ; Appenzeller, Simone ; Reis, Fabiano ; Pereira, Danilo Rodrigues ; Bento, Mariana Pinheiro ; Rittner, Leticia ; Colliot, O ; Isgum, I ; Landman, BA ; Loew, MH

• Document type: Journal article
• Source: MEDICAL IMAGING 2022: IMAGE PROCESSING; v. 12032, p. 7-pg., 2022-01-01.

Abstract

The Corpus callosum (CC) is a massive white matter structure in the brain, and changes in its shape and volume are associated with subject characteristics, several diseases, and clinical conditions. The CC is mostly studied in magnetic resonance imaging (MRI), where it is segmented to extract valuable information. With the increasing availability of MRI data and the proliferation of automated algorithms to perform CC segmentation, quality control (QC) verification is mandatory to assure reliability in the entire analysis pipeline. We propose a convolutional neural network (CNN) for QC of CC segmentations. The CNN gets information on the mask and contextual information on the image and performs deep feature extraction using a pre-trained model. The CNN model was fine-tuned using T-1 -weighted MR images with CC masks, in the task of classifying correct or incorrect segmentations. The CNN-based approach got an area under the curve (AUC) of 97.98% on the test set. We used an additional test set of patients with tumor to test generalization capability of the trained model to other domains. (AU)

• FAPESP's process: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology
• Grantee: Fernando Cendes
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC