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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


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.
Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings
EditorsXiaohuan Cao, Xi Ouyang, Xuanang Xu, Islem Rekik, Zhiming Cui
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
Volume14349 LNCS
ISBN (Print)9783031456756
Publication statusPublished - 2024
Event14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023 - Vancouver, Canada
Duration: 8 Oct 20238 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14349 LNCS


Conference14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023


  • Cerebrovascular diseases
  • Ellipsoid bounding shapes
  • Fast R-CNN
  • enlarged perivascular spaces

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