ABSTRACT
Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.
Subject(s)
Algorithms , Histological TechniquesABSTRACT
The case of a 39-year-old man with slowly growing mass in the superior part of left parotid region is described. Patient presented neurological symptoms including hypomobility of lower left eyelid and inability of complete closure of left side eyelids resulting in conjunctivitis and hyperlacrimation. Routine physical examination supported by image and laboratory tests was performed. Pathomorphological results of hematoxylin and eosin staining as well immunohistochemical examination in view of clinical presentation pointed to diagnosis of high grade salivary duct carcinoma. Rare incidence, histological view similar to breast cancer and body localization are sufficient reasons for further analyses and descriptions of this type of lesions.
