Academic Journal
Fast and scalable annotation-free LV-centered ROI localisation in stress perfusion cardiac MRI.
| Τίτλος: | Fast and scalable annotation-free LV-centered ROI localisation in stress perfusion cardiac MRI. |
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| Συγγραφείς: | Kalashami MP; School of Engineering, University of Leicester, Leicester, United Kingdom., Fagioli A; Unitelma Sapienza University of Rome, Rome, Italy., Marini MR; Department of Computer Science, Sapienza University of Rome, VisionLab, Rome, Italy., Elshibly M; Department of Cardiovascular Sciences, University of Leicester, NIHR Leicester Biomedical Research Centre, British Heart Foundation Centre of Research Excellence, Glenfield Hospital, Leicester, United Kingdom., Shergill S; Department of Cardiovascular Sciences, University of Leicester, NIHR Leicester Biomedical Research Centre, British Heart Foundation Centre of Research Excellence, Glenfield Hospital, Leicester, United Kingdom., McCann GP; Department of Cardiovascular Sciences, University of Leicester, NIHR Leicester Biomedical Research Centre, British Heart Foundation Centre of Research Excellence, Glenfield Hospital, Leicester, United Kingdom., Arnold JR; Department of Cardiovascular Sciences, University of Leicester, NIHR Leicester Biomedical Research Centre, British Heart Foundation Centre of Research Excellence, Glenfield Hospital, Leicester, United Kingdom., Statharas D; School of Engineering, University of Leicester, Leicester, United Kingdom. Electronic address: ds708@leicester.ac.uk. |
| Πηγή: | Computers in biology and medicine [Comput Biol Med] 2026 Jul 15; Vol. 211, pp. 111747. Date of Electronic Publication: 2026 May 19. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York : Elsevier Original Publication: New York, Pergamon Press. |
| Ιατρικοί όροι (MeSH): | Heart Ventricles*/diagnostic imaging , Image Processing, Computer-Assisted*/methods, Humans ; Perfusion Magnetic Resonance Imaging |
| Περίληψη: | Fast and scalable localisation of a left ventricle (LV)-centered region of interest (ROI) is essential for cardiac image analysis, particularly in stress perfusion cardiovascular magnetic resonance (CMR), where manual annotation is time-consuming and labelled datasets are scarce. This study proposes a fully automated, annotation-free pipeline that integrates Fast Fourier Transform-based localisation, Sobel edge refinement, and a fallback heuristic to generate consistent LV-centered ROIs directly from raw perfusion frames, without requiring task-specific annotations. A circular ROI is applied as a post-processing step to ensure consistent coverage of the ventricular cavity and surrounding myocardium while preserving diagnostically relevant boundary information. To assess whether learning-based models can replicate these pseudo-annotations without perfusion-specific training, a clinician-verified subset of 295 FFT-Sobel annotations was used to benchmark a pretrained U-Net and a one-shot similarity model. The FFT + Sobel method achieved successful ROI localisation in 81.5% of 460 frames at 0.002 s per frame. The U-Net achieved a Dice score of 88.6% and IoU of 80.1%, but only 72% full ROI detection, while the one-shot model showed limited spatial agreement (Dice 26.7%, IoU 16.8%). These findings demonstrate that the proposed classical pipeline provides a fast, robust, and scalable solution for LV-centered ROI localisation in annotation-free perfusion CMR, offering a consistent and clinically meaningful representation for downstream cardiac image analysis. (Copyright © 2026. Published by Elsevier Ltd.) |
| Competing Interests: | Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mahsa Pourhossein Kalashami reports financial support was provided by University of Leicester Future 100 Scholarship. Mahsa Pourhossein Kalashami reports financial support was provided by Touring Scheme Funding. J. Ranjit Arnold reports a relationship with NIHR (National Institute for Health and Care Research) that includes: funding grants. Gerry P. McCann reports a relationship with NIHR Research Professorship (RP-2017-08-ST2-007) that includes: funding grants. J. Ranjit Arnold reports a relationship with NIHR Clinician Scientist Award (CS-2018-18-ST2-007) that includes: funding grants. J. Ranjit Arnold reports a relationship with NIHR Research for Patient Benefit (NIHR201460) that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Σχόλια: | Erratum in: Comput Biol Med. 2026 Jul 29:111880. doi: 10.1016/j.compbiomed.2026.111880.. (PMID: 42527203) |
| Contributed Indexing: | Keywords: Annotation-free segmentation; Automated image annotation; Left ventricle localisation; Medical image processing; ROI detection in MRI |
| Entry Date(s): | Date Created: 20260519 Date Completed: 20260716 Latest Revision: 20260729 |
| Update Code: | 20260730 |
| DOI: | 10.1016/j.compbiomed.2026.111747 |
| PMID: | 42155374 |
| Βάση Δεδομένων: | MEDLINE |
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