Statistical shape modelling is a powerful tool for visualising and quantifying geometric and functional patterns of the heart.
Data
The training dataset will comprise one hundred (100) cases with myocardial infarction and an additional one hundred (100) asymptomatic cases from the DETERMINE and MESA datasets respectively, contributed to the Cardiac Atlas Project (www.cardiacatlas.org).
Shapes will be provided as corresponding Cartesian point sets in cardiac MRI magnet coordinates at end-diastole (ED) and end-systole (ES). Classification labels indicating disease (0 = normal, 1 = infarcted) will be provided for the training dataset. No images will be provided.
The goals are to:
- Establish a statistical shape model from the set of 3D shapes
- Classify cases between normal or abnormal (with myocardial infarct)
The participants’ methods will be tested in a different set of 200 cases, again containing 100 asymptomatic cases and 100 infarcted cases. Classification accuracy and related measures of agreement will be calculated (specificity, sensitivity, etc.).
It is expected that the probabilistic models can be easily visualised but there is no restriction on the type of decomposition used to partition the shape space (i.e. can be linear or non-linear). Both supervised and non-supervised classification methods can be submitted (if there are enough of both a comparison might be made). A peer-reviewed paper summarising the findings of this challenge will be submitted to an appropriate journal.
Results
Extract from the collation paper:
Statistical shape modeling of the left ventricle: myocardial infarct classification challenge
Pau Medrano-Gracia, Xingyu Zhang, Avan Suinesiaputra, Brett Cowan and Alistair A. Young
Classifications results from the validation dataset achieved a median 93% specificity, sensitivity and accuracy (Table 2). Accuracy ranged from 73 to 98%.
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Challenge paper title
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First Author
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Specificity
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Sensitivity
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Accuracy
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A
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Systo-diastolic LV shape analysis by Geometric Morphometrics and Parallel Transport highly discriminates myocardial infarction
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Paolo Piras
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93
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97
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95
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B
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Statistical Shape Modeling using Partial Least Squares: Application to Myocardial Infarction Assessment
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Karim Lekadir
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99
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97
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98
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C
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Classification of Myocardial Infarcted Patients by Combining Shape and Motion Features
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Wenjia Bai
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97
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94
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95.5
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D
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Detecting Myocardial Infarction using Medial Surfaces
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Pierre Ablin
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89
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90
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89.5
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E
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Left ventricle classification using Active Shape Model and Support Vector Machine
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Nripesh Parajuli
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97
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93
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95
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F
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Supervised Learning of Functional Maps for Infarction Classification
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Anirban Mukhopadhyay
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73
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73
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73
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G
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Joint Clustering and Component Analysis of Spatio-Temporal Shape Patterns in Myocardial Infarction
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Catarina Pinto
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94
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86
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90
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H
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Myocardial Infarction Detection from Left Ventricular Shapes using a Random Forest
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Jack Allen
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91
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92
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91.5
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I
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Combination of Polyaffine Transformations and Supervised Learning for the Automatic Diagnosis of LV Infarct
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Marc-Michel Rohé
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95
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95
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95
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J
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Automatic detection of cardiac remodeling using global and local clinical measures and random forest classification
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Jan Ehrhardt
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89
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92
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90.5
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K
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Automatic Detection of Myocardial Infarction Through a Global Shape Feature Based on Local Statistical Modeling
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Mahdi Tabassian
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89
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97
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93
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Median
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93
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93
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93
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Availability
We are still working on collating the results of this challenge. The data will be available after publication.