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This challenge aims at creating an open and fair competition for various research groups to test and validate their methods, particularly for the multi-sequence ventricle and myocardium segmentation.

Skin cancer is the most common cancer globally, with melanoma being the most deadly form. Dermoscopy is a skin imaging modality that has demonstrated improvement for diagnosis of skin cancer compared to unaided visual inspection. However, clinicians should receive adequate training for those improvements to be realized.

BraTS has always been focusing on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans. BraTS 2019 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas.

The goal of the challenge is to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). There are two tasks and therefore two leaderboards for evaluating the performance of the algorithms. Participants can choose to join both or either tasks according to their interests.

CHAOS has two separate but related aims:

  1. Segmentation of liver from computed tomography (CT) data sets, which are acquired at portal phase after contrast agent injection for pre-evaluation of living donated liver transplantation donors (15 training + 15 test sets).
  2. Segmentation of four abdominal organs (i.e. liver, spleen, right and left kidneys) from magnetic resonance imaging (MRI) data sets acquired with two different sequences (T1-DUAL and T2-SPIR) (15 training + 15 test sets).

Digital pathology has been gradually introduced in clinical practice. Although the digital pathology scanner could give very high resolution whole-slide images (WSI) (up to 160nm per pixel), the manual analysis of WSI is still a time-consuming task for the pathologists. Automatic analysis algorithms offer a way to reduce the burden for pathologists. Our proposed challenge will focus on automatic detection and classification of lung cancer using Whole-slide Histopathology. This subject is highly clinical relevant because lung cancer is the top cause of cancer-related death in the world.

In digital pathology, it is often useful to align spatially close but differently stained tissue sections in order to obtain the combined information. The images are large, in general, their appearance and their local structure are different, and they are related through a nonlinear transformation. The proposed challenge focuses on comparing the accuracy and approximative speed of automatic non-linear registration methods for this task. Registration accuracy will be evaluated using manually annotated landmarks.

In 2012, Cell Tracking Challenge (CTC) was launched to objectively compare and evaluate state-of-the-art whole-cell and nucleus segmentation and tracking methods using both real (2D and 3D) time-lapse microscopy videos of cells and nuclei, along with computer generated (2D and 3D) video sequences simulating nuclei moving in realistic environments. To address numerous requests for benchmarking only cell segmentation methods (without tracking), we are launching now a new time-lapse cell segmentation benchmark on the same datasets (plus one new dataset).

Computer assisted tools can provide cost effective and easily deployable solutions for cancer diagnostics. The aim of this challenge is to build a classifier for the identification of leukemic versus normal immature cells for while blood cancer, namely, B-ALL diagnostics. A dataset of cells with class labels, marked by the expert based on the domain knowledge, will be provided at the subject-level to train the classifier. This problem is interesting because the two cell types appear similar under the microscope and subject-level variability plays a key role.

The PALM challenge focuses on investigation and development of algorithms associated with diagnosis of Pathologic Myopia (PM) and segmentation of lesions in fundus photos from PM patients. Myopia is currently the ocular disease with the highest morbidity. About 2 billion people have myopia in the world, 35% of which are high myopia. High myopia leads to elongation of axial length and thinning of retinal structures. With progression of the disease into PM, macular retinoschisis, retinal atrophy and even retinal detachment may occur, causing irreversible impairment to visual acuity.


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