PMID- 32562100
OWN - NLM
STAT- MEDLINE
DCOM- 20210127
LR  - 20210127
IS  - 1534-3170 (Electronic)
IS  - 1523-3782 (Linking)
VI  - 22
IP  - 8
DP  - 2020 Jun 19
TI  - Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from 
      Magnetic Resonance Imaging.
PG  - 65
LID - 10.1007/s11886-020-01321-1 [doi]
AB  - PURPOSE OF REVIEW: Myocardial fibrosis (MF) arises due to myocardial infarction 
      and numerous cardiac diseases. MF may lead to several heart disorders, such as 
      heart failure, arrhythmias, and ischemia. Cardiac magnetic resonance (CMR) 
      imaging techniques, such as late gadolinium enhancement (LGE) CMR, enable 
      non-invasive assessment of MF in the left ventricle (LV). Manual assessment of MF 
      on CMR is a tedious and time-consuming task that is subject to high observer 
      variability. Automated segmentation and quantification of MF is important for 
      risk stratification and treatment planning in patients with heart disorders. This 
      article aims to review the machine learning (ML)-based methodologies developed 
      for MF quantification in the LV using CMR images. RECENT FINDINGS: With the 
      availability of relatively large labeled datasets supervised learning methods 
      based on both conventional ML and state-of-the-art deep learning (DL) methods 
      have been successfully applied for automated segmentation of MF. The 
      incorporation of ML algorithms into imaging techniques such as 3D LGE CMR permits 
      fast characterization of MF on CMR imaging and may enhance the diagnosis and 
      prognosis of patients with heart disorders. Concurrently, the studies using cine 
      CMR images have revealed that accurate segmentation of MF on non-contrast CMR 
      imaging might be possible. The application of ML/DL tools in CMR image 
      interpretation is likely to result in accurate and efficient quantification of 
      MF.
FAU - Zabihollahy, Fatemeh
AU  - Zabihollahy F
AUID- ORCID: 0000-0003-3362-1009
AD  - Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, 
      Canada. fatemehzabihollahy@cmail.carleton.ca.
FAU - Rajan, S
AU  - Rajan S
AD  - Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, 
      Canada.
FAU - Ukwatta, E
AU  - Ukwatta E
AD  - School of Engineering, University of Guelph, Guelph, ON, Canada.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Review
DEP - 20200619
PL  - United States
TA  - Curr Cardiol Rep
JT  - Current cardiology reports
JID - 100888969
RN  - 0 (Contrast Media)
RN  - AU0V1LM3JT (Gadolinium)
SB  - IM
MH  - *Contrast Media
MH  - Fibrosis
MH  - Gadolinium
MH  - *Heart Ventricles/pathology
MH  - Humans
MH  - Machine Learning
MH  - Magnetic Resonance Imaging
MH  - Magnetic Resonance Imaging, Cine
MH  - Myocardium/pathology
MH  - Predictive Value of Tests
OTO - NOTNLM
OT  - Cardiac magnetic resonance imaging
OT  - Deep learning
OT  - Late gadolinium enhancement
OT  - Machine learning
OT  - Myocardial fibrosis
EDAT- 2020/06/21 06:00
MHDA- 2021/01/28 06:00
CRDT- 2020/06/21 06:00
PHST- 2020/06/21 06:00 [entrez]
PHST- 2020/06/21 06:00 [pubmed]
PHST- 2021/01/28 06:00 [medline]
AID - 10.1007/s11886-020-01321-1 [pii]
AID - 10.1007/s11886-020-01321-1 [doi]
PST - epublish
SO  - Curr Cardiol Rep. 2020 Jun 19;22(8):65. doi: 10.1007/s11886-020-01321-1.