Academic Journal
ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation
| Title: | ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation |
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| Authors: | Hang Zhang, Jinwei Zhang, Chao Li, Elizabeth M. Sweeney, Pascal Spincemaille, Thanh D. Nguyen, Susan A. Gauthier, Yi Wang, Melanie Marcille |
| Source: | NeuroImage: Clinical, Vol 32, Iss , Pp 102854- (2021) |
| Publisher Information: | Elsevier, 2021. |
| Publication Year: | 2021 |
| Collection: | LCC:Computer applications to medicine. Medical informatics LCC:Neurology. Diseases of the nervous system |
| Subject Terms: | Multiple sclerosis, Convolutional neural network, Lesion segmentation, MRI, Deep learning, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429 |
| More Details: | 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 |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2213-1582 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2213158221002989; https://doaj.org/toc/2213-1582 |
| DOI: | 10.1016/j.nicl.2021.102854 |
| Access URL: | https://doaj.org/article/3024c9fe42d14faf9ddf4f1dae125ccb |
| Accession Number: | edsdoj.3024c9fe42d14faf9ddf4f1dae125ccb |
| Database: | Directory of Open Access Journals |
| ISSN: | 22131582 |
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| DOI: | 10.1016/j.nicl.2021.102854 |
| Published in: | NeuroImage: Clinical |
| Language: | English |