Leveraging Ellipsoid Bounding Shapes and Fast R-CNN for Enlarged Perivascular Spaces Detection and Segmentation

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

  • Mariam Zabihi
  • Chayanin Tangwiriyasakul
  • Silvia Ingala
  • Luigi Lorenzini
  • Robin Camarasa
  • Frederik Barkhof
  • de Bruijne, Marleen
  • M. Jorge Cardoso
  • Carole H. Sudre

Enlarged perivascular spaces (EPVS) are small fluid-filled spaces surrounding blood vessels in the brain. They have been found to be important in the development and progression of cerebrovascular disease, including stroke, dementia, and cerebral small vessel disease. Their accurate detection and quantification are crucial for early diagnosis and better management of these diseases. In recent years, object detection techniques such as Mask R-CNN approach have been widely used to automate the detection and segmentation of small objects. To account for the tubular shape of these markers we use ellipsoid shapes instead of bounding boxes to express the location of individual elements in the implementation of the Fast R-CNN. We investigate the performance of this model under different modality combinations and find that the T2 modality alone, as well as the combination of T1+T2, deliver better performance.

OriginalsprogEngelsk
TitelMachine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings
RedaktørerXiaohuan Cao, Xi Ouyang, Xuanang Xu, Islem Rekik, Zhiming Cui
ForlagSpringer
Publikationsdato2024
Sider325-334
ISBN (Trykt)9783031456756
DOI
StatusUdgivet - 2024
Begivenhed14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023 - Vancouver, Canada
Varighed: 8 okt. 2023 → 8 okt. 2023

Konference

Konference14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023
LandCanada
ByVancouver
Periode08/10/2023 → 08/10/2023
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind14349 LNCS
ISSN0302-9743

Bibliografisk note

Funding Information:
Wellcome Trust (082464/Z/07/Z), British Heart Foundation (SP/07/001/23603, PG/08/103, PG/12/29/29497 and CS/13/1/30327), Erasmus MC University Medical Center, the Erasmus University Rotterdam, the Netherlands Organization for Scientific Research (NWO) Grant 918-46-615, the Netherlands Organization for Health Research and Development (ZonMW), the Research Institute for Disease in the Elderly (RIDE), and the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 601055, VPHDARE@IT, the Dutch Technology Foundation STW. This study was also supported by (WT203148/Z/16/Z) and Wellcome Flagship Programme (WT213038/Z/18/Z). MZ and CHS are supported by an Alzheimer’s Society Junior Fellowship (AS-JF-17-011). SI and LL have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under Amyloid Imaging to Prevent Alzheimer’s Disease (AMYPAD) grant agreement No. 115952 and European Prevention of Alzheimer’s Dementia (EPAD) grant No. 115736. This Joint Undertaking receives the support from the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA. Rc and MdB is supported by Netherlands Organisation for NWO project VI.C.182.042.

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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