Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential.

Digital pathology represents one of the major evolutions in modern medicine. Pathological examinations constitute the gold standard in many medical protocols, and also play a critical and legal role in the diagnosis process. In the conventional cancer diagnosis, pathologists analyze biopsies to make diagnostic and prognostic assessments, mainly based on the cell morphology and architecture distribution. Recently, computerized methods have been rapidly evolving in the area of digital pathology, with growing applications related to nuclei detection, segmentation, and classification. In cancer research, these approaches have played, and will continue to play a key (often bottleneck) role in minimizing human intervention, consolidating pertinent second opinions, and providing traceable clinical information. Pathological studies have been conducted for numerous cancer detection and grading applications, including brain, breast, cervix, lung, and prostate cancer grading. Our study presents, discusses, and extracts the major trends from an exhaustive overview of various nuclei detection, segmentation, feature computation, and classification techniques used in histopathology imagery, specifically in hematoxylin-eosin and immunohistochemical staining protocols. This study also enables us to measure the challenges that remain, in order to reach robust analysis of whole slide images, essential high content imaging with diagnostic biomarkers and prognosis support in digital pathology.

Multi-channels statistical and morphological features based mitosis detection in breast cancer histopathology.

Accurate counting of mitosis in breast cancer histopathology plays a critical role in the grading process. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. This work aims at improving the accuracy of mitosis detection by selecting the color channels that better capture the statistical and morphological features having mitosis discrimination from other objects. The proposed framework includes comprehensive analysis of first and second order statistical features together with morphological features in selected color channels and a study on balancing the skewed dataset using SMOTE method for increasing the predictive accuracy of mitosis classification. The proposed framework has been evaluated on MITOS data set during an ICPR 2012 contest and ranked second from 17 finalists. The proposed framework achieved 74% detection rate, 70% precision and 72% F-Measure. In future work, we plan to apply our mitosis detection tool to images produced by different types of slide scanners, including multi-spectral and multi-focal microscopy.

Mitosis detection in breast cancer histological images An ICPR 2012 contest.

INTRODUCTION: In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study. Indeed, mitosis detection is difficult because mitosis are small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. We added a further dimension to the contest by using two different slide scanners having different resolutions and producing red-green-blue (RGB) images, and a multi-spectral microscope producing images in 10 different spectral bands and 17 layers Z-stack. 17 teams participated in the study and the best team achieved a recall rate of 0.7 and precision of 0.89. CONTEXT: Several studies on automatic tools to process digitized slides have been reported focusing mainly on nuclei or tubule detection. Mitosis detection is a challenging problem that has not yet been addressed well in the literature. AIMS: Mitotic count is an important parameter in breast cancer grading as it gives an evaluation of the aggressiveness of the tumor. However, consistency, reproducibility and agreement on mitotic count for the same slide can vary largely among pathologists. An automatic tool for this task may help for reaching a better consistency, and at the same time reducing the burden of this demanding task for the pathologists. SUBJECTS AND METHODS: Professor Frιdιrique Capron team of the pathology department at Pitiι-Salpκtriθre Hospital in Paris, France, has selected a set of five slides of breast cancer. The slides are stained with H and E. They have been scanned by three different equipments: Aperio ScanScope XT slide scanner, Hamamatsu NanoZoomer 2.0-HT slide scanner and 10 bands multispectral microscope. The data set is made up of 50 high power fields (HPF) coming from 5 different slides scanned at ×40 magnification. There are 10 HPFs/slide. The pathologist has annotated all the mitotic cells manually. A HPF has a size of 512 µm × 512 µm (that is an area of 0.262 mm (2) , which is a surface equivalent to that of a microscope field diameter of 0.58 mm. These 50 HPFs contain a total of 326 mitotic cells on images of both scanners, and 322 mitotic cells on the multispectral microscope. RESULTS: Up to 129 teams have registered to the contest. However, only 17 teams submitted their detection of mitotic cells. The performance of the best team is very promising, with F-measure as high as 0.78. However, the database we provided is by far too small for a good assessment of reliability and robustness of the proposed algorithms. CONCLUSIONS: Mitotic count is an important criterion in the grading of many types of cancers, however, very little research has been made on automatic mitotic cell detection, mainly because of a lack of available data. A main objective of this contest was to propose a database of mitotic cells on digitized breast cancer histopathology slides to initiate works on automated mitotic cell detection. In the future, we would like to extend this database to have much more images from different patients and also for different types of cancers. In addition, mitotic cells should be annotated by several pathologists to reflect the partial agreement among them.

Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach.

CONTEXT: According to Nottingham grading system, mitosis count in breast cancer histopathology is one of three components required for cancer grading and prognosis. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. AIMS: The aim is to investigate the various texture features and Hierarchical Model and X (HMAX) biologically inspired approach for mitosis detection using machine-learning techniques. MATERIALS AND METHODS: We propose an approach that assists pathologists in automated mitosis detection and counting. The proposed method, which is based on the most favorable texture features combination, examines the separability between different channels of color space. Blue-ratio channel provides more discriminative information for mitosis detection in histopathological images. Co-occurrence features, run-length features, and Scale-invariant feature transform (SIFT) features were extracted and used in the classification of mitosis. Finally, a classification is performed to put the candidate patch either in the mitosis class or in the non-mitosis class. Three different classifiers have been evaluated: Decision tree, linear kernel Support Vector Machine (SVM), and non-linear kernel SVM. We also evaluate the performance of the proposed framework using the modified biologically inspired model of HMAX and compare the results with other feature extraction methods such as dense SIFT. RESULTS: The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS) dataset provided for an International Conference on Pattern Recognition (ICPR) 2012 contest. The proposed framework achieved 76% recall, 75% precision and 76% F-measure. CONCLUSIONS: Different frameworks for classification have been evaluated for mitosis detection. In future work, instead of regions, we intend to compute features on the results of mitosis contour segmentation and use them to improve detection and classification rate.

Time-efficient sparse analysis of histopathological whole slide images.

Histopathological examination is a powerful standard for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of whole slide images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI. It relies on application-driven for high-resolution and generic for low-resolution image analysis algorithms embedded in a multi-scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading. GPU technologies as well speed up the global time-efficiency of the system. Sparse coding and dynamic sampling constitute the keystone of our approach. These methods are implemented within a computer-aided breast biopsy analysis application based on histopathology images and designed in collaboration with a pathology department. The current ground truth slides correspond to about 36,000 high magnification (40×) high power fields. The processing time to achieve automatic WSI analysis is on a par with the pathologist's performance (about ten minutes a WSI), which constitutes by itself a major contribution of the proposed methodology.

A cognitive virtual microscopic framework for knowledge-based exploration of large microscopic images in breast cancer histopathology.

Histopathological examination is a powerful method for prognosis of major diseases such as breast cancer. Analysis of medical images largely remains the work of human experts. Current virtual microscope systems are mainly an emulation of real microscopes with annotation and some image analysis capabilities. However, the lack of effective knowledge management prevents such systems from being computer-aided prognosis platforms. The cognitive virtual microscopic framework, through an extended modeling and use of medical knowledge, has the capacity to analyse histopathological images and to perform grading of breast cancer, providing pathologists with a robust and traceable second opinion.