  Accurate detection of mitosis plays a critical role in breast cancer
histopathology. Manual detection and counting of mitosis is tedious and subject
to considerable inter- and intra-reader variations. Multispectral imaging is a
recent medical imaging technology, proven successful in increasing the
segmentation accuracy in other fields. This study aims at improving the
accuracy of mitosis detection by developing a specific solution using
multispectral and multifocal imaging of breast cancer histopathological data.
We propose to enable clinical routine-compliant quality of mitosis
discrimination from other objects. The proposed framework includes
comprehensive analysis of spectral bands and z-stack focus planes, detection of
expected mitotic regions (candidates) in selected focus planes and spectral
bands, computation of multispectral spatial features for each candidate,
selection of multispectral spatial features and a study of different
state-of-the-art classification methods for candidates classification as
mitotic or non mitotic figures. This framework has been evaluated on MITOS
multispectral medical dataset and achieved 60% detection rate and 57%
F-Measure. Our results indicate that multispectral spatial features have more
information for mitosis classification in comparison with white spectral band
features, being therefore a very promising exploration area to improve the
quality of the diagnosis assistance in histopathology.
