Journal article
Automated detection of cerebral microbleeds on MR images using knowledge distillation framework
- Abstract:
-
Introduction: Cerebral microbleeds (CMBs) are associated with white matter damage, and various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility-weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate automated detection of CMBs would help to determine quantitative imaging biomarkers (e.g., CMB count) on large datasets. In this work, we propose a fully automated, deep learning-based, 3-step algorithm, using structural and anatomical properties of CMBs from any single input image modality (e.g., GRE/SWI/QSM) for their accurate detections.
Methods: In our method, the first step consists of an initial candidate detection step that detects CMBs with high sensitivity. In the second step, candidate discrimination step is performed using a knowledge distillation framework, with a multi-tasking teacher network that guides the student network to classify CMB and non-CMB instances in an offline manner. Finally, a morphological clean-up step further reduces false positives using anatomical constraints. We used four datasets consisting of different modalities specified above, acquired using various protocols and with a variety of pathological and demographic characteristics.
Results: On cross-validation within datasets, our method achieved a cluster-wise true positive rate (TPR) of over 90% with an average of <2 false positives per subject. The knowledge distillation framework improves the cluster-wise TPR of the student model by 15%. Our method is flexible in terms of the input modality and provides comparable cluster-wise TPR and better cluster-wise precision compared to existing state-of-the-art methods. When evaluating across different datasets, our method showed good generalizability with a cluster-wise TPR >80 % with different modalities. The python implementation of the proposed method is openly available.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 3.6MB, Terms of use)
-
- Publisher copy:
- 10.3389/fninf.2023.1204186
Authors
- Funder identifier:
- https://ror.org/029chgv08
- Grant:
- 203141/Z/16/Z
- 202788/Z/16/Z
- 203139/Z/16/Z
- 215573/Z/19/Z
- 215573/Z/19/Z
- Funder identifier:
- https://ror.org/00k4n6c32
- Grant:
- 666881
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/L016052/1
- Publisher:
- Frontiers Media
- Journal:
- Frontiers in Neuroinformatics More from this journal
- Volume:
- 17
- Article number:
- 1204186
- Place of publication:
- Switzerland
- Publication date:
- 2023-07-10
- Acceptance date:
- 2023-06-19
- DOI:
- EISSN:
-
1662-5196
- Pmid:
-
37492242
- Language:
-
English
- Keywords:
- Pubs id:
-
1499121
- Local pid:
-
pubs:1499121
- Deposit date:
-
2024-12-24
Terms of use
- Copyright holder:
- Sundaresan et al.
- Copyright date:
- 2023
- Rights statement:
- © 2023 Sundaresan, Arthofer, Zamboni, Murchison, Dineen, Rothwell, Auer, Wang, Miller, Tendler, Alfaro-Almagro, Sotiropoulos, Sprigg, Grianti and Jenkinson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Notes:
- This research was funded in part by the Wellcome Trust (203139/Z/16/Z). For the purpose of open access, the author has applied a CC-BY public copyright licence to any Author Accepted manuscript version arising from this submission.
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record