Challenges will take place on Friday, April 3.
Challenge 1: White Matter Microstructure with Diffusion MRI Challenge (PM Session)
Bennett A. Landman: bennett.landman@vanderbilt.edu
Kurt G. Schilling: kurt.g.schilling.1@vumc.org
Challenge 2: Multi-organ Nuclei Segmentation And Classification Challenge 2020 (PM Session)
Abstract: In this challenge, participants will be provided with H&E stained tissue images of four organs with annotations of multiple cell-types including epithelial cells, lymphocytes, macrophages, and neutrophils. Participants will use the annotated dataset to develop computer vision algorithms to recognize these cell-types from the tissue images of unseen patients released in the testing set of the challenge. Additionally, all cell-types will not have equal number of annotated instances in the training dataset which will encourage the participants to develop algorithms for learning from imbalanced classes in a few shot learning paradigm.
Ruchika Verma: verma.ece.ruchika@gmail.com
Challenge 3: Endoscopy vision challenge on segmentation and detection (AM Session)
Abstract: Smart endoscopy requires automated solutions to tackle inevitable artefacts such as motion blur, organ specularities, illumination variabilities and sensor artefacts. Once the fundamental artefact problem is addressed, it is also of eminent interest to detect automatically anomalies present in the organ under surveillance. EndoCV2020 challenge thus introduces two core sub-themes in endoscopy: 1) artefact detection and segmentation (EAD2020) and 2) disease detection and segmentation (EDD2020).
Sharib Ali: sharib.ali@eng.ox.ac.uk
Felix Zhou: felix.zhou@ludwig.ox.ac.uk
Challenge 4: AccelMR 2020 (PM Session)
*The training data is available now for the challenge.
Abstract: The Accel-MR 2020 Challenge invites researcher to submit their method to define the non-linear mapping for low-resolution acquired at multiple resolutions and high-resolution image pairs. Magnetic Resonance Imaging (MRI) is an imaging technique used in daily practice to acquire diagnostic images of different organs of the body especially brain. Its unique features such as superior soft-tissue contrast, elimination of ionizing radiation, and accurate response to functional changes make it an exceptionally well-accepted tool to aid clinical diagnosis especially in pediatric applications. However, due to the underlying physics that governs the generation of images with MRI, its imaging speed is relatively slow compared with other widely used imaging modalities (e.g. CT and ultrasound), making it uncomfortable for patients. Moreover, sedation or general anesthesia, needed for young children to reduce motion artifacts due to longer scanning time, have shown to affect neurodevelopment due to the neurotoxicity stemming from anesthetic agents. Therefore, it is desirable to accelerate the image acquisition speed with MRI but without sacrificing image quality.
Awais Mansoor: accelmr2020@gmail.com
Challenge 5: Diabetic Retinopathy Assessment Grading and Diagnosis (AM Session)
Abstract: Automated machine learning can facilitate the early diagnosis and timely treatment of diabetic retinopathy. Following the 1st Diabetic Retinopathy: Segmentation and Grading Challenge held with ISBI in 2018, we would like to promote the progress further through 2nd challenge using a new dataset, Deep Diabetic Retinopathy Image Dataset (DeepDRiD). The challenge is subdivided into three tasks as follows:
A. Dual-View Disease Grading: Classification of fundus images according to the severity level of diabetic retinopathy using dual view retinal fundus images.
B. Image Quality Estimation: Fundus quality assessment for overall image quality, artifacts, clarity, and field definition.
C. Transfer Learning: Explore the generalizability of a Diabetic Retinopathy (DR) grading system. The robust and generalizable models are expected to be developed to solve clinical issues in reality.
Dr. Bin Sheng: shengbin@deepdrdoc.com
Dr. Ping Zhang: zhang.10631@osu.edu
Challenge 6: 5th ISBI Cell Tracking Challenge (PM Session)
Abstract: The Cell Tracking Challenge is a well-established competition, with the primary aim of objectively benchmarking state-of-the-art cell segmentation and tracking methods over a diverse and annotated repository of multidimensional time-lapse image data of cells and nuclei captured using different light microscopy modalities. In its fifth edition, the scope of the challenge will be broadened by adding two datasets from a previously not covered modality of bright-field microscopy and one difficult dataset of developing Tribolium Castaneum embryo (full 3D+time videos as compared to their 3D cartographic projections released in the fourth edition last year). Finally, a silver segmentation ground truth corpus will be released for the training videos of nine previously existing datasets to facilitate the tuning of competing methods.
Carlos Ortiz de Solórzano: codesolorzano@unav.es
Michal Kozubek: kozubek@fi.muni.cz
Challenge 7: Automatic Detection challenge on Age-related Macular degeneration (PM Session)
Abstract: Age-related macular degeneration, abbreviated as AMD, is a degenerative disorder in the macular region. It mainly occurs in people older than 45 years old and its incidence rate is even higher than diabetic retinopathy in the elderly. Early diagnosis of AMD is crucial to treatment and prognosis. Fundus photo is one of the basic examinations. This dataset is composed of AMD and non-AMD (myopia, normal control, etc.) photos. Typical signs of AMD that can be found in these photos include drusen, exudation, hemorrhage, etc.
Yanwu Xu: ywxu@ieee.org