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Table 71 Recent studies describing fully automated LV or RV segmentation algorithm based on Convolutional Neural Networks (CNN)

From: Reference ranges (“normal values”) for cardiovascular magnetic resonance (CMR) in adults and children: 2020 update

Author, year

Segmented structure

Data used for training/validation

Validation methods/remarks

Tran, 2017 [203]

LV + RV

LV: MICCAI-2009 (n = 45)

RV: RVSC-2012 (n = 45)

Validation: auto vs manual

Metrics: DICE, HD, ACD

Bai, 2018 [2]

LV + RV

Including LV, RV, LA, RA from long-axis cine

4875 subjects

UK Biobank cohort

Multi-center, single vendor

Validation: auto vs manual

Metrics: DICE, HD, APD

LV: EDV, ESV, EF, SV, CO, LV mass

RV: EDV, ESV, EF, CO

Bernard, 2018 [198]

LV + RV

ACDC-2017 (n = 150)

Nine methods compared

Validation: auto vs manual

Metrics: DICE, HD, CLBR

LV: EDV, ESV, EF, LV mass

RV: EDV, ESV, EF

Khened, 2018 [200]

LV + RV

KAGGLE-2015 (n = 1140)

ACDC-2017 (n = 150)

LVSC-2011 (n = 200)

Validation: auto vs manual

Metrics: DICE, CLBR

Patient diagnosis

Tan, 2018 [201]

LV

LVSC-2011 (n = 200)

KAGGLE-2015 (n = 1140)

Validation: auto vs manual

Metrics: DICE, JI, HD

EDV, ESV

Tao, 2018 [3]

LV

Training: 400 subjects

Testing: 150 subjects

Multi-center, multi-vendor

Multiple patient categories: MI (n = 322), DCM (n = 168), HCM (n = 23), DCM (n = 23), PH (n = 10) other (n = 27), normal (n = 23)

Validation: auto vs manual

Metrics: DICE

EDV, ESV, EF, LV mass

Vigneault, 2018 [204]

LV + RV from multiple views

53 subjects

HCM (n = 42), healthy (n = 21)

ACDC-2017 (n = 150)

Validation: auto vs manual

Metrics: DICE

Backhaus, 2019 [212]

LV + RV

Evaluation of SuiteHEART software (Neosoft)

300 randomly selected patients used for validation

Single center

1.5 T and 3 T data

Validation: auto vs manual

LV: EDV, ESV, SV, EF, LV mass

RV: EDV, ESV, EF

Bhuva, 2019 [197]

LV

Training data: 599 subjects

Test data 110 patients, 5 disease categories: myocardial infarction (n = 32), LVH (n = 17), cardiomyopathy (n = 17), other pathology (n = 14), healthy volunteers (n = 30)

Multi-center, multi-vendor, 1.5 T + 3 T

Scan-rescan data

Data availability:

https://www.thevolumesresource.com

Validation based on comparing scan-rescan reproducibility of automated vs manual analysis

EDV, ESV, SV, EF, LV mass

Detectable change in EF

Curiale, 2019 [199]

LV

MICCAI-2009 (n = 45)

Cardiac Atlas Project (n = 95)

Validation: auto vs manual

Metrics: DICE

EDV, ESV, EF, LV mass

Tong, 2019 [202]

LV + RV

ACDC-2017 (n = 150)

Validation: auto vs manual

Metrics: Dice, HD

LV: EDV, ESV, EF, LV mass

RV: EDV, ESV, EF

  1. Recent studies (> 2017) describing fully automated LV or RV segmentation algorithm based on Convolutional Neural Networks (CNN), which were validated either on publicly available data sets, or using lager (> 300 subjects) single-center or multi-center clinical patient cohorts. Segmentation is performed from short-axis cine MR, except stated otherwise.
  2. LV left ventricle, RV right ventricle, LA left atrium, RA right atrium, MI myocardial infarction, DCM dilated cardiomyopathy, HCM hypertrophic cardiomyopathy, PH pulmonary hypertension, DICE dice overlap metric, HD Hausdorff distance, JI Jaccard index, CLBR challenge leader board ranking, ACD average contour distance, MICCAI Medical Image Computing and Computer Assisted Intervention, LVSC Left Ventricle Segmentation Challenge, ACDC Automatic Cardiac Diagnosis Challenge.