Histologic tumor grading systems assess the differentiation of the tumors, i.e., how closely they resemble normal tissue when examined under a microscope. Generally, patients with well-differentiated tumors have better outcomes and vice versa. Clinicians use the histologic grade, among other factors, to give an estimate of the patient’s prognosis and to develop individual treatment plans.  The most widely used system for histologic grading of invasive breast cancer (BC) is the Bloom & Richardson grading system (B&R). It consists of three components: nuclear pleomorphism, degree of tubule formation and mitotic activity.

Mitotic activity is one of the strongest prognosticators for invasive breast carcinoma.  It is expressed as the number of mitotic figures per tissue area within the histological slides. As part of the B&R grading system, mitotic activity is routinely assessed in pathology labs across the world. In addition, the mitotic activity can be used as a prognosticator independently of the B&R grading system. Although it has strong prognostic value for invasive breast carcinoma, it is a tedious task prone to observer variability. With the advent of digital imaging in pathology, which has enabled cost and time efficient digitization of whole histological slides, automatic image analysis has been suggested as a way to tackle these problems.

Challenge goals

The goal of this challenge is to evaluate and compare (semi-)automatic mitotic figure detection methods that work on regions extracted from whole-slide images. It is our strong belief that providing open access to a high quality annotated dataset can lead to major advancement in the development of a successful mitotic detection method.

Since only the number of mitotic figures present in the tissue is of importance (i.e. the size and shape of the mitotic figures is not of interest), we formulated the challenge as a detection problem. The ground truth is provided in the form of locations of ground truth objects and the results are requested in the same format.

In the spirit of cooperative scientific progress, we want to evaluate whether different methods work better in different situations, and whether a combination of methods and ideas can result in improvement of the results.

Challenge format

Teams or individuals interested in participating in the challenge can register on this website. Registered participants can download a training set consisting of images and ground truth locations of mitotic figures, which they can use to develop their method, and a testing set consisting of images only. The participants will be able to run their method on the testing set and upload results on this website for evaluation. All submissions must be accompanied by a short description of the method. The submitted results will be evaluated periodically (see below).

For more information, please refer to the BackgroundDatasetEvaluation and Rules pages.

The AMIDA13 workshop was held on September 22nd, 2013 as part of MICCAI 2013 in Nagoya, Japan. It consisted of presentations of the proposed methods by the participants and summary of the results by the organizers. You can view the results from the workshop on the Results page. We are currently working on a paper with overview of all the methods that were presented at the workshop and summary of the results.


The submitted results are evaluated on a weekly basis. You will be informed by e-mail when your evaluation results become available.


Mitko Veta, Max A. Viergever, Josien P.W. Pluim
Image Sciences Institute, University Medical Center Utrecht

Nikolaos Stathonikos, Paul J. van Diest
Pathology Department, University Medical Center Utrecht