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ISBI 2013 Grand Challenges Announced!

posted Nov 26, 2012, 8:47 AM by Stephen Aylward

The 2013 ISBI Grand Challenges will be held on the last day of the conference (Thursday, April 11th, 2013). 

The grand challenges chosen for ISBI 2013 are the following: 

  1. HARDI Reconstruction Challenge
  2. 3D Deconvolution Microscopy Challenge
  3. Computer Aided Detection of Pulmonary Embolism (CADPE)
  4. 3D Segmentation of Neurites in EM Images
  5. Automated Segmentation of Prostate Structures
  6. Localization Microscopy Challenge
  7. Cell Tracking Challenge

Additional details on these challenges are given below.  The organization of each challenge is unique.  The tasks, metrics, participation requirements, and important dates are given in the links provided.  In general, each challenges is a combination of a contest and a workshop.  The contest involves processing ground-truth data and is held during the months preceding the conference.  During the conference workshop, additional data may need to be processes, participants will have the opportunity to present their methods, and all attendees will be invited to discuss the results.

We look forward to your participation in the ISBI Grand Challenges!

2013 ISBI Grand Challenges Chairs
            Stephen Aylward (Kitware)
            Bram van Ginneken (Radboud University Nijmegen)

 

1. HARDI Reconstruction Challenge

Validation is the bottleneck for the diffusion magnetic resonance imaging (MRI) community. In the last few years a multitude of new reconstruction approaches have been proposed to recover the local intra-voxel fiber structure: some of them aim at improving the quality of the reconstructions while others focus on reducing the acquisition time. However, when a new algorithm is proposed, the performances are normally assessed with ad hoc synthetic data and evaluation criteria, and comparing different approaches can be difficult.

The HARDI (high angular resolution diffusion imaging) reconstruction challenge is organized with the aim to provide all researchers in this field with a common framework to assess the performances of their reconstruction schemes and fairly compare their results under controlled conditions.

The main goal of this contest is to investigate not only the accuracy in the local estimation of the intra-voxel fiber configuration of each algorithm, but also its impact on subsequent global connectivity analyses.

http://hardi.epfl.ch/static/events/2013_ISBI/

 

2. 3D Deconvolution Microscopy Challenge

Deconvolution is one of the most common image-reconstruction tasks that arise in 3D fluorescence microscopy. The aim of this challenge is to benchmark existing deconvolution algorithms and to stimulate the community to look for novel, global and practical approaches to this problem.

The challenge will be organized as a three-stage tournament (training, qualification and final). It will be based on novel realistic-looking synthetic data sets representing various subcellular structures. In addition it will rely on a number of common and advanced performance metrics to objectively assess the quality of the results.

http://bigwww.epfl.ch/deconvolution/challenge/

 

3. Computer Aided Detection of Pulmonary Embolism (CADPE)

A pulmonary (thrombo) embolism (PE) refers to the situation when a portion of a blood clot becomes lodged in a pulmonary artery. PE is diagnosed with computed tomography pulmonary angiography (CTPA). Delay of treatment or lack of treatment results in increased morbidity and mortality. In comparison to an expert panel, the average radiologist misses visible emboli. Computer aided detection (CAD) algorithms have been shown to increase radiologists’ sensitivity. However, CAD has not been adopted into clinical practice due to the large amount of false positive detections that those algorithms produce. We hope that this challenge enables new CAD algorithms with lower false positive rate than state-of-the-art and makes CAD for PE a reality.

http://www.cadpe.org

 

4. 3D Segmentation of Neurites in EM Images

In this challenge, a full stack of electron microscopy (EM) slices will be used to train machine-learning algorithms for the purpose of automatic segmentation of neurites in 3D. This imaging technique visualizes the resulting volumes in a highly anisotropic way, i.e., the x- and y-directions have a high resolution, whereas the z-direction has a low resolution, primarily dependent on the precision of serial cutting. EM produces the images as a projection of the whole section, so some of the neural membranes that are not orthogonal to a cutting plane can appear very blurred. None of these problems led to major difficulties in the manual labeling of each neurite in the image stack by an expert human neuro-anatomist. The aim of the challenge is to compare and rank the different competing methods based on their object classification accuracy in three dimensions.

Website is under construction.  Please email iarganda@mit.edu for more information.


5. Automated Segmentation of Prostate Structures

The Cancer Imaging Program of the National Cancer Institute (NCI) in collaboration with the International Society for Biomedical Imaging (ISBI) will launch a grand challenge in segmentation of internal structures of the prostate gland based on magnetic resonance imaging data.  The overall goal of this challenge is to promote the development of robust open and closed source codes that can automatically identify the peripheral zone and the central gland from volumetric T2-weighted MR image sets.   Data for the prostate challenge will be provided by Boston University and Radboud University, Nijmegen Medical Centre (the Netherlands).  The collection, consisting of training and test data, will be hosted on the Cancer Image Archive (TCIA), a publicly available NCI resource.

http://cancerimagingarchive.net/

 

6. Localization Microscopy Challenge

Super-resolution imaging is a recent emerging field of microscopy that improves the resolution of the conventional microscopy by an order of magnitude. It allows one to observe and study biological structures at the molecular scale. The most popular technique for super-resolution microscopy is the single molecule localization imaging, which localizes individual molecules that are spatially and temporally separated one form the other. Super-resolution data is reconstructed by image-analysis software, and the reconstruction quality is highly dependent on the algorithm used and the software implementation.

The goal of this challenge is to benchmark currently available localization software, in particular, comparing their performance in terms of detection rate, localization accuracy, and computation time. Accessibility and usability for the end-user will be also evaluated. The benchmark data consists of simulated images of various biological structure such as tubulins. The simulated data accounts for fluorophores activation and excitation, image formation, and known perturbations models. As the ground truth exact positions of the fluorophores are known, the benchmarking metrics will use objective measures.

The challenge is open to all individuals or teams, academic or corporate, that developed or are currently developing super-resolution software. The participants should have full knowledge and expertise on the software they introduce.

The challenge and its results will be summarized in a scientific publication with the participants as coauthors.

http://bigwww.epfl.ch/smlm/challenge/


7. Cell Tracking Challenge

Tracking moving cells in time-lapse video sequences is a challenging task, required for many applications in both scientific and industrial settings. Properly characterizing how cells move as they interact with their surrounding environment is key to understanding the mechanobiology of cell migration and its multiple implications in both normal tissue development and many diseases.

In this challenge we call for submission of existing and newly developed cell tracking algorithms. These algorithms will be evaluated and compared in terms of cell segmentation and cell lineage accuracy, as well as the time required for the analysis of the time-lapse sequences.

The datasets will consist of fluorescence microscopy 2D and 3D time-lapse videos containing varying cell types and/or embedding environments, stained either nuclear or cytoplasmically. Furthermore, synthetic time-lapse sequences of 2D and 3D nuclei moving in realistic environments will be used to further test the performance of the algorithms under different noise and cell density conditions.

The algorithms will be ranked based on their performance on each particular dataset. The results will be presented at the ISBI challenge workshop and will be reported also in a high impact factor journal paper.

http://www.codesolorzano.com/celltrackingchallenge/Cell_Tracking_Challenge/Welcome.html 

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