Multispectral Spatial Characterization: Application to Mitosis Detection in Breast Cancer Histopathology

TL;DR

This paper presents a multispectral imaging framework for improving mitosis detection accuracy in breast cancer histopathology, leveraging spectral and focus plane analysis to enhance classification performance.

Contribution

It introduces a novel multispectral spatial feature-based approach combined with classification methods for mitosis detection in histopathological images.

Findings

Achieved 60% detection rate and 57% F-Measure on MITOS dataset.

Multispectral spatial features outperform white spectral band features.

Demonstrated potential for improved diagnostic assistance in histopathology.

Abstract

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.