LV Statistical Shape Modelling Challenge

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%.

Table 2. Participants methods were tested in a separate validation set. All values are %.
 
Challenge paper title
First Author
Specificity
Sensitivity
Accuracy
A
Systo-diastolic LV shape analysis by Geometric Morphometrics and Parallel Transport highly discriminates myocardial infarction
Paolo Piras
93
97
95
B
Statistical Shape Modeling using Partial Least Squares: Application to Myocardial Infarction Assessment
Karim Lekadir
99
97
98
C
Classification of Myocardial Infarcted Patients by Combining Shape and Motion Features
Wenjia Bai
97
94
95.5
D
Detecting Myocardial Infarction using Medial Surfaces
Pierre Ablin
89
90
89.5
E
Left ventricle classification using Active Shape Model and Support Vector Machine
Nripesh Parajuli
97
93
95
F
Supervised Learning of Functional Maps for Infarction Classification
Anirban Mukhopadhyay
73
73
73
G
Joint Clustering and Component Analysis of Spatio-Temporal Shape Patterns in Myocardial Infarction
Catarina Pinto
94
86
90
H
Myocardial Infarction Detection from Left Ventricular Shapes using a Random Forest
Jack Allen
91
92
91.5
I
Combination of Polyaffine Transformations and Supervised Learning for the Automatic Diagnosis of LV Infarct
Marc-Michel Rohé
95
95
95
J
Automatic detection of cardiac remodeling using global and local clinical measures and random forest classification
Jan Ehrhardt
89
92
90.5
K
Automatic Detection of Myocardial Infarction Through a Global Shape Feature Based on Local Statistical Modeling
Mahdi Tabassian
89
97
93
 
Median
 
93
93
93

Availability

nothing

The data set of this challenge is temporarily closed for download !

We are still working on collating the results of this challenge. The data will be available after publication.