Meeting Banner
Abstract #4708

NigrosomeNet: An Automatic Framework to Quantify Nigrosomal Neurodegeneration in Substantia Nigra Using Deep Learning

Sapna Mishra1,2, Isidora Jankovic2,3, Priyanka Bhat4, Rivka Bendrihem5, Tapan Kumar Gandhi1, Senthil Kumaran4, Stéphane Lehéricy2, Nicolas Villain2,6, Aurelie Kas7, Nadya Pyatigorskaya2, and Rahul Gaurav2
1Department of Electrical Engineering, Indian Institute of Technology, Delhi, India, 2Sorbonne University, Paris Brain Institute (ICM), Paris, France, 3Center for Radiology, University Clinical Center Nis, Nis, Serbia, France, 4Department of Nuclear Magnetic Resonance, All India Institute of Medical Sciences, Delhi, India, 5Centre d'Imagerie Médicale Italie, Paris, France, 6Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Paris, France, 7Department of Nuclear Medicine, Pitié-Salpêtrière Hospital, AP-HP, Paris, France

Synopsis

Keywords: AI/ML Software, Machine Learning/Artificial Intelligence, Nigrosome, Parkinson's disease, Alzheimer’s disease, swallow tail sign

Motivation: Early differential diagnosis of parkinsonism-related and unrelated disorders, particularly dementia with Lewy bodies vs Alzheimer’s, is crucial for clinical management. Nigrosome 1 (N1) roughly corresponds to the dorsolateral nigral hyperintensity visible on iron-sensitive imaging, aids in identifying early parkinsonian neurodegeneration.

Goal(s): Developing an automatic tool for N1 segmentation enhancing diagnostic speed and accuracy for neurodegenerative disorders.

Approach: NigrosomeNet employs convolutional neural network, trained and validated on MRI data, to automatically segment N1. Validation included dice similarity comparisons with expert annotations.

Results: NigrosomeNet achieved fast and high segmentation accuracy (dice coefficient of 0.96 for parkinsonian patients), showing a significant N1 volume difference between groups.

Impact: NigrosomeNet provides a rapid, reliable, and rater-independent solution for N1 analysis, enhancing diagnostic accuracy in clinical settings. This tool could significantly streamline neurodegenerative disease management, support large-scale studies, and reduce the need for specialized training, making early diagnosis more accessible.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords