ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation.

TitleALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation.
Publication TypeJournal Article
Year of Publication2021
AuthorsZhang H, Zhang J, Li C, Sweeney EM, Spincemaille P, Nguyen TD, Gauthier SA, Wang Y, Marcille M
JournalNeuroimage Clin
Volume32
Pagination102854
Date Published2021
ISSN2213-1582
KeywordsAlgorithms, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Mental Recall, Multiple Sclerosis, Neural Networks, Computer
Abstract

Accurate detection and segmentation of multiple sclerosis (MS) brain lesions on magnetic resonance images are important for disease diagnosis and treatment. This is a challenging task as lesions vary greatly in size, shape, location, and image contrast. The objective of our study was to develop an algorithm based on deep convolutional neural network integrated with anatomic information and lesion-wise loss function (ALL-Net) for fast and accurate automated segmentation of MS lesions. Distance transformation mapping was used to construct a convolutional module that encoded lesion-specific anatomical information. To overcome the lesion size imbalance during network training and improve the detection of small lesions, a lesion-wise loss function was developed in which individual lesions were modeled as spheres of equal size. On the ISBI-2015 longitudinal MS lesion segmentation challenge dataset (19 subjects in total), ALL-Net achieved an overall score of 93.32 and was amongst the top performing methods. On the larger Cornell MS dataset (176 subjects in total), ALL-Net significantly improved both voxel-wise metrics (Dice improvement of 3.9% to 35.3% with p-values ranging from p < 0.01 to p < 0.0001, and AUC of voxel-wise precision-recall curve improvement of 2.1% to 29.8%) and lesion-wise metrics (lesion-wise F1 score improvement of 12.6% to 29.8% with all p-values p < 0.0001, and AUC of lesion-wise ROC curve improvement of 1.4% to 20.0%) compared to leading publicly available MS lesion segmentation tools.

DOI10.1016/j.nicl.2021.102854
Alternate JournalNeuroimage Clin
PubMed ID34666289
PubMed Central IDPMC8521204
Grant ListR01 NS104283 / NS / NINDS NIH HHS / United States
R01 NS105144 / NS / NINDS NIH HHS / United States
Related Institute: 
MRI Research Institute (MRIRI)

Weill Cornell Medicine
Department of Radiology
525 East 68th Street New York, NY 10065