MICCAI Grand challenge: Assessment of mitosis detection algorithms (AMIDA13)

format The abstract should be 500 to 1000 words long, and contain Methods and Experiments sections. The Methods section should contain a short overview of the proposed method, in sufficient detail to understand how the method works. If a commercial system is used a method description is not necessary, but the exact name of the system and version number need to be provided. The Experiments section should describe the steps taken in order to select the detection model and/or model parameters (training procedure). Evaluation measures A detection will be considered a true positive if it’s Eucledian distance to a ground truth location is less than 7.5 μm (30 pixels). It can happen that multiple detections fall within 7.5 μm of a single ground truth location. In that case, they will be counted as one true positive. All detections that are not within 7.5 μm of a ground truth location will be counted as false positives. All ground truth locations that do not have a detection within 7.5 μm will be counted as false negatives. For comparison of the proposed methods, two different rankings will be produced: • Ranking according to the overall F1-score; • Ranking according to the F1-score computed for each patient separately; In the first ranking scheme, all ground truth objects are considered as a single dataset (regardless to which patient they belong to). The proposed methods will simply be ranked according to the F1-score calculated as F1 = 2·precision·recall / (precision + recall). The first ranking scheme is heavily influenced by the results for the cases with very high number of mitotic figures. The second ranking scheme equally weights the results from all cases, regardless of the number of mitotic figures present in them. In this case, the ground truth objects belonging to a single patient are considered as separate datasets. F1-score is calculated on the patient level, and the proposed methods are ranked for each patient separately. The final placing of the methods is according to the average ranking from all patients. In the training dataset there is one case with zero ground truth mitotic figures. If such cases occur in the testing dataset, the ranking for those cases will be done according to the number of false positive detections, as the precision and recall are not defined. The ranking of the semi-automatic methods will be done separately from the automatic methods. The final analysis of the results, that will be part of the presentation at the workshop and the overview paper, will also include a qualitative evaluation. This will include review of the most common false positive detections and false negatives by additional expert