Mitosis Extraction in Breast-Cancer Histopathological Whole Slide Images

Roullier, Vincent; Lézoray, Olivier; Ta, Vinh-Thong; Elmoataz, Abderrahim

doi:10.1007/978-3-642-17289-2_52

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Anari et al [4] proposed fuzzy cmean clustering algorithm along with ultra-erosion operation in Lab color space for detection of proliferative nuclei and mitosis index in IHC images of meningioma. Roullier et al [5] proposed a multi-resolution unsupervised clustering driven by domain specific knowledge that resulted in more than 70% sensitivity and 80% specificity. The extraction of textural features from nuclear regions represents the variation in chromatin arrangement and, hence different stages in the nuclei cycle [6].…”

Section: Previous Workmentioning

confidence: 99%

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

Irshad

Roux

Racoceanu

2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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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.

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Section: Previous Workmentioning

confidence: 99%

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

Irshad

Roux

Racoceanu

2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…Our top-down segmentation algorithm uses the same scheme than [9] with the additional use of a tiled top-down pyramid ( Figure 6). Successive k-means based classifications [7] allow to label and refine regions of interest.…”

Section: Application For Large Histological Images Segmentationmentioning

confidence: 99%

Tiled Top-Down Pyramids and Segmentation of Large Histological Images

Goffe¹,

Brun²,

Damiand³

2011

Graph-Based Representations in Pattern Recognition

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To cite this version:Romain Goffe, Luc Brun, Guillaume Damiand. Tiled top-down pyramids and segmentation of large histological images. Springer. In 8th IAPR -TC-15 Workshop on Graph-based Representations in Pattern Recognition (GBR'11), May 2011, Munster, Germany. 6658, pp.255-264, 2011 Abstract Recent microscopic imaging systems such as whole slide scanners provide very large (up to 18GB) high resolution images. Such amounts of memory raise major issues that prevent usual image representation models from being used. Moreover, using such high resolution images, global image features, such as tissues, do not clearly appear at full resolution. Such images contain thus different hierarchical information at different resolutions. This paper presents the model of tiled top-down pyramids which provides a framework to handle such images. This model encodes a hierarchy of partitions of large images defined at different resolutions. We also propose a generic construction scheme of such pyramids whose validity is evaluated on an histological image application.

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A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches

Rahaman

et al. 2022

Artif Intell Rev

158

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Mitosis Extraction in Breast-Cancer Histopathological Whole Slide Images

Cited by 4 publications

References 10 publications

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

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

Tiled Top-Down Pyramids and Segmentation of Large Histological Images

A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches

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