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.

Diffusion MRI has emerged as a key modality for imaging brain tissue microstructural features, yet, validation is necessary for accurate and useful biomarkers. Towards this end, we present the two-year ISBI 2019/2020 diffusion Mri whitE Matter rEcoNstrucTiOn (MEMENTO) challenge. The first year is dedicated to designing the challenge, building the appropriate dataset(s), and making it available to the community. The challenge and participant submissions will take place in the second year, with the aim to evaluate and advance the state of the microstructural modeling field.

Endoscopic Artefact Detection (EAD) is a core challenge in facilitating diagnosis and treatment of diseases in hollow organs. Precise detection of specific artefacts like pixel saturations, motion blur, specular reflections, bubbles and debris is essential for high-quality frame restoration and is crucial for realising reliable computer-assisted tools for improved patient care.