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Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis
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- Åkesson, Julius (författare)
- Lund University,Lunds universitet,Avdelningen för Biomedicinsk teknik,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Klinisk fysiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Hjärt-MR-gruppen i Lund,Forskargrupper vid Lunds universitet,LTH profilområde: Teknik för hälsa,LTH profilområden,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH,Clinical Physiology (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Lund Cardiac MR Group,Lund University Research Groups,LTH Profile Area: Engineering Health,LTH Profile areas,Faculty of Engineering, LTH
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- Ostenfeld, Ellen (författare)
- Lund University,Lunds universitet,Klinisk fysiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Hjärt-MR-gruppen i Lund,Forskargrupper vid Lunds universitet,Clinical Physiology (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Lund Cardiac MR Group,Lund University Research Groups
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- Carlsson, Marcus (författare)
- Lund University,Lunds universitet,Klinisk fysiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Hjärt-MR-gruppen i Lund,Forskargrupper vid Lunds universitet,Clinical Physiology (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Lund Cardiac MR Group,Lund University Research Groups
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- 2023-01-21
- 2023
- Engelska.
- Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 13:1
- Tidskriftsartikel (refereegranskat)
Abstract
Ämnesord
Stäng
- Right ventricular (RV) volumes are commonly obtained through time-consuming manual delineations of cardiac magnetic resonance (CMR) images. Deep learning-based methods can generate RV delineations, but few studies have assessed their ability to accelerate clinical practice. Therefore, we aimed to develop a clinical pipeline for deep learning-based RV delineations and validate its ability to reduce the manual delineation time. Quality-controlled delineations in short-axis CMR scans from 1114 subjects were used for development. Time reduction was assessed by two observers using 50 additional clinical scans. Automated delineations were subjectively rated as (A) sufficient for clinical use, or as needing (B) minor or (C) major corrections. Times were measured for manual corrections of delineations rated as B or C, and for fully manual delineations on all 50 scans. Fifty-eight % of automated delineations were rated as A, 42% as B, and none as C. The average time was 6 min for a fully manual delineation, 2 s for an automated delineation, and 2 min for a minor correction, yielding a time reduction of 87%. The deep learning-based pipeline could substantially reduce the time needed to manually obtain clinically applicable delineations, indicating ability to yield right ventricular assessments faster than fully manual analysis in clinical practice. However, these results may not generalize to clinics using other RV delineation guidelines.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
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- Åkesson, Julius
- Ostenfeld, Ellen
- Carlsson, Marcus
- Arheden, Håkan
- Heiberg, Einar
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