## About Dataset

### Left Atrial Segmentation Challenge

**Authors**: Catalina Tobon-Gomez (catactg@gmail.com) and Arjan Geers (ajgeers@gmail.com)

### About

This repository is associated with the Left Atrial Segmentation Challenge 2013 (LASC'13). LASC'13 was part of the STACOM'13 workshop, held in conjunction with MICCAI'13. Seven international research groups, comprising 11 algorithms, participated in the challenge.

For a detailed report, please refer to:

Tobon-Gomez C, Geers AJ, Peters, J, Weese J, Pinto K, Karim R, Ammar M, Daoudi A, Margeta J, Sandoval Z, Stender B, Zheng Y, Zuluaga, MA, Betancur J, Ayache N, Chikh MA, Dillenseger J-L, Kelm BM, Mahmoudi S, Ourselin S, Schlaefer A, Schaeffter T, Razavi R, Rhode KS. Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets. IEEE Transactions on Medical Imaging, 34(7):1460–1473, 2015.

The challenge is also featured on Cardiac Atlas Project.

The Python scripts in this repository take as input a segmentation and output the two evaluation metrics described in the paper.

The data and code of the challenge have been made publicly available to serve as a benchmark for left atrial segmentation algorithms.

Feel free to contact us with any questions.

### Abbreviations

* CT: Computed tomography

* GT: Ground truth

* MRI: Magnetic resonance imaging

* LA: Left atrium

* LASC'13: Left Atrial Segmentation Challenge 2013

* PV: Pulmonary vein

### Data

The benchmark consists of 30 CT and 30 MRI datasets. Per modality, 10 datasets are for training of segmentation algorithms and 20 datasets are for testing.

The MRI datasets are publicly available on Figshare:

* Training

* Testing

* Results

The data agreement for CT datasets expired on September 2018. Therefore, we can not share these datasets anymore.

Sample data from an arbitrary modality/institute/case were included in this repository to be able to run the scripts.

### Abstract

The knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance. More recently, LA anatomical models have been used for cardiac biophysical modelling. Segmentation of the LA from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is a complex problem. We aimed at evaluating current algorithms that address this problem by creating a unified benchmarking framework through the mechanism of a challenge, the Left Atrial Segmentation Challenge 2013 (LASC’13). Thirty MRI and thirty CT datasets were provided to participants for segmentation. Ten data sets for each modality were provided with expert manual segmentations for algorithm training. The other 20 data sets per modality were used for evaluation. The datasets were provided by King’s College London and Philips Technologie GmbH. Each participant segmented the LA including a short part of the LA appendage trunk plus the proximal parts of the pulmonary veins. Details on the evaluation framework and the results obtained in this challenge are presented in this manuscript. The results showed that methodologies combining statistical models with region growing approaches were the most appropriate to handle the proposed task.