Different types of cells play a vital role in the initiation, development, invasion, metastasis and therapeutic response of tumors of various organs. For example, (1) most carcinomas originate from epithelial cells, (2) spatial arrangement of tumor infiltrating Lymphocytes (TILs) is associated with clinical outcome in several cancers, including the ones of breast, prostate, and lung (Fridman et. al., Nature Reviews Cancer, 2012), and (3) tumor associated macrophages (TAMs) influence diverse processes such as angiogenesis, neoplastic cell mitogenesis, antigen presentation, matrix degradation, and cytotoxicity in various tumors (Ruffel and Coussens, Cancer Cell, 2015). Thus, accurate identification and segmentation of nuclei of multiple cell-types is important for AI enabled characterization of tumor and its microenvironment.
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 participants to develop algorithms for learning from imbalanced classes in a few shot learning paradigm.
H&E staining of human tissue sections is a routine and most common protocol used by pathologists to enhance the contrast of tissue sections for tumor assessment (grading, staging, etc.) at multiple microscopic resolutions. Hence, we will provide the annotated dataset of H&E stained digitized tissue images of several patients acquired at multiple hospitals using one of the most common 40x scanner magnification. The annotations will be done with the help of expert pathologists.