scholarly journals Development of neoplastic region selection algorithm based on breast cancer whole slide image

Doklady BGUIR ◽  
2020 ◽  
Vol 18 (8) ◽  
pp. 21-28
Author(s):  
S. N. Rjabceva ◽  
V. A. Kovalev ◽  
V. D. Malyshev ◽  
I. A. Siamionik ◽  
M. A. Derevyanko ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Operating Time ◽  
Color Distribution ◽  
Whole Slide Image ◽  
Image Descriptors ◽  
Region Selection ◽  
Slide Image ◽  
Image Area ◽  
High Degree ◽  
Whole Slide Images

Analysis of breast cancer whole-slide image is an extremely labor-intensive process. Histological whole slide images have the following features: a high degree of tissue diversity both in one image and between different images, hierarchy, a large amount of graphic information and different artifacts. In this work, pre-processing of breast cancer whole-slide tissue image was carried out, which included normalization of the color distribution and the image area selection. We reduced the operating time of the other algorithms and excluded areas of breast cancer whole-slide tissue with a background to analyze. Also, an algorithm for finding similar neoplastic regions for semi-automatic selection using various image descriptors has been developed and implemented.

scholarly journals Defining the area of mitoses counting in invasive breast cancer using whole slide image

2021 ◽  
Author(s):  
Asmaa Ibrahim ◽  
Ayat G. Lashen ◽  
Ayaka Katayama ◽  
Raluca Mihai ◽  
Graham Ball ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Practical Method ◽  
Relative Increase ◽  
Significant Rise ◽  
Mitotic Count ◽  
Morphological Criteria ◽  
Slide Image ◽  
Hematoxylin And Eosin ◽  
Mitotic Counting ◽  
Whole Slide Images

AbstractAlthough counting mitoses is part of breast cancer grading, concordance studies showed low agreement. Refining the criteria for mitotic counting can improve concordance, particularly when using whole slide images (WSIs). This study aims to refine the methodology for optimal mitoses counting on WSI. Digital images of 595 hematoxylin and eosin stained sections were evaluated. Several morphological criteria were investigated and applied to define mitotic hotspots. Reproducibility, representativeness, time, and association with outcome were the criteria used to evaluate the best area size for mitoses counting. Three approaches for scoring mitoses on WSIs (single and multiple annotated rectangles and multiple digital high-power (×40) screen fields (HPSFs)) were evaluated. The relative increase in tumor cell density was the most significant and easiest parameter for identifying hotspots. Counting mitoses in 3 mm2 area was the most representative regarding saturation and concordance levels. Counting in area <2 mm2 resulted in a significant reduction in mitotic count (P = 0.02), whereas counting in area ≥4 mm2 was time-consuming and did not add a significant rise in overall mitotic count (P = 0.08). Using multiple HPSF, following calibration, provided the most reliable, timesaving, and practical method for mitoses counting on WSI. This study provides evidence-based methodology for defining the area and methodology of visual mitoses counting using WSI. Visual mitoses scoring on WSI can be performed reliably by adjusting the number of monitor screens.

scholarly journals CytoBrowser: a browser-based collaborative annotation platform for whole slide images

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 226
Author(s):  
Christopher Rydell ◽  
Joakim Lindblad
Keyword(s):  
Large Scale ◽  
Image Data ◽  
Ground Truth ◽  
Free Form ◽  
Remote Collaboration ◽  
Whole Slide Image ◽  
Server Side ◽  
Collaborative Annotation ◽  
Slide Image ◽  
Whole Slide Images

We present CytoBrowser, an open-source (GPLv3) JavaScript and Node.js driven environment for fast and accessible collaborative online visualization, assessment, and annotation of very large microscopy images, including, but not limited to, z-stacks (focus stacks) of cytology or histology whole slide images. CytoBrowser provides a web-based viewer for high-resolution zoomable images and facilitates easy remote collaboration, with options for joint-view visualization and simultaneous collaborative annotation of very large datasets. It delivers a unique combination of functionalities not found in other software solutions, making it a preferred tool for large scale annotation of whole slide image data. The web browser interface is directly accessible on any modern computer or even on a mobile phone, without need for additional software. By sharing a "session", several remote users can interactively explore and jointly annotate whole slide image data, thereby reaching improved data understanding and annotation quality, effortless project scaling and distribution of resources to/from remote locations, efficient creation of "ground truth" annotations for methods' evaluation and training of machine learning-based approaches, a user-friendly learning environment for medical students, to just name a few. Rectangle and polygon region annotations complement point-based annotations, each with a selectable annotation-class as well as free-form text fields. The default setting of CytoBrowser presents an interface for the Bethesda cancer grading system, while other annotation schemes can easily be incorporated. Automatic server side storage of annotations is complemented by JSON-based import/export options facilitating easy interoperability with other tools. CytoBrowser is available here: https://mida-group.github.io/CytoBrowser/.

scholarly journals Gaussian Process Based Heteroscedastic Noise Modeling for Tumor Mutation Burden Prediction from Whole Slide Images

2019 ◽  
Author(s):  
Sunho Park ◽  
Hongming Xu ◽  
Tae Hyun Hwang
Keyword(s):  
Gaussian Process ◽  
Cancer Patients ◽  
Ordinal Regression ◽  
Noise Variance ◽  
Tumor Mutation Burden ◽  
Whole Slide Image ◽  
Noise Modeling ◽  
Mutation Burden ◽  
Slide Image ◽  
Whole Slide Images

Tumor mutation burden (TMB) is a quantitative measurement of how many mutations present in tumor cells from a patient tumor as assessed by next-generation sequencing (NGS) technology. High TMB is used as a predictive biomarker to select patients that likely respond to immunotherapy in many cancer types, thus it is critical to accurately measure TMB for cancer patients who need to receive the immunotherapy. Recent studies showed that image features from histopathology whole slide images can be used to predict genetic features (e.g., mutation status) or clinical outcome of cancer patients. In this study, we develop a computational method to predict the TMB level from cancer patients’ histopathology whole slide images. The prediction problem is formulated as multiple instance learning (MIL) because a whole slide image (a bag) has to be divided into multiple image blocks (instances) due to computational reasons but a single label is available only to an entire whole slide image not to each image block. In particular, we propose a novel heteroscedastic noise model for MIL based on the framework of Gaussian process (GP), where the noise variance is assumed to be a latent function of image level features. This noise variance can encode the confidence in predicting the TMB level from each training image and make the method to put different levels of effort to classify images according to how difficult each image can be correctly classified. The method tries to fit an easier image well while it does not put much effort in classifying a harder (ambiguous) image correctly. Expectation and propagation (EP) is employed to efficiently infer our model and to find the optimal hyper-parameters. We have demonstrated from synthetic and real-world data sets that our method outperforms on TMB prediction from whole slide images base-line methods, including a special case of our method that does not include the heteroscedastic noise modeling and multiple instance ordinal regression (MIOR) that is one of few algorithms to solve ordinal regression in the MIL setting.

scholarly journals Pathology Trainees’ Experience and Attitudes on Use of Digital Whole Slide Images

2020 ◽  
Vol 7 ◽  
pp. 237428952095192
Author(s):  
Joann G. Elmore ◽  
Hannah Shucard ◽  
Annie C. Lee ◽  
Pin-Chieh Wang ◽  
Kathleen F. Kerr ◽  
...  
Keyword(s):  
Medical School ◽  
Online Survey ◽  
Medical Training ◽  
Primary Diagnosis ◽  
Curricular Integration ◽  
Whole Slide Image ◽  
Diagnostic Use ◽  
Positive Attitudes ◽  
Slide Image ◽  
Whole Slide Images

Digital whole slide images are Food and Drug Administration approved for clinical diagnostic use in pathology; however, integration is nascent. Trainees from 9 pathology training programs completed an online survey to ascertain attitudes toward and experiences with whole slide images for pathological interpretations. Respondents (n = 76) reported attending 63 unique medical schools (45 United States, 18 international). While 63% reported medical school exposure to whole slide images, most reported ≤ 5 hours. Those who began training more recently were more likely to report at least some exposure to digital whole slide image training in medical school compared to those who began training earlier: 75% of respondents beginning training in 2017 or 2018 reported exposure to whole slide images compared to 54% for trainees beginning earlier. Trainees exposed to whole slide images in medical school were more likely to agree they were comfortable using whole slide images for interpretation compared to those not exposed (29% vs 12%; P = .06). Most trainees agreed that accurate diagnoses can be made using whole slide images for primary diagnosis (92%; 95% CI: 86-98) and that whole slide images are useful for obtaining second opinions (93%; 95% CI: 88-99). Trainees reporting whole slide image experience during training, compared to those with no experience, were more likely to agree they would use whole slide images in 5 years for primary diagnosis (64% vs 50%; P = .3) and second opinions (86% vs 76%; P = .4). In conclusion, although exposure to whole slide images in medical school has increased, overall exposure is limited. Positive attitudes toward future whole slide image diagnostic use were associated with exposure to this technology during medical training. Curricular integration may promote adoption.

scholarly journals A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Marc Aubreville ◽  
Christof A. Bertram ◽  
Taryn A. Donovan ◽  
Christian Marzahl ◽  
Andreas Maier ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Human Breast Cancer ◽  
Representation Learning ◽  
Breast Cancer Dataset ◽  
Human Breast ◽  
Cancer Dataset ◽  
Slide Image ◽  
Similar Appearance ◽  
Mitotic Figures ◽  
Whole Slide Images

AbstractCanine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset.

scholarly journals Agreement in Histological Assessment of Mitotic Activity Between Microscopy and Digital Whole Slide Images Informs Conversion for Clinical Diagnosis

2019 ◽  
Vol 6 ◽  
pp. 237428951985984 ◽  
Author(s):  
Bih-Rong Wei ◽  
Charles H. Halsey ◽  
Shelley B. Hoover ◽  
Munish Puri ◽  
Howard H. Yang ◽  
...  
Keyword(s):  
Mitotic Activity ◽  
Primary Diagnosis ◽  
Mitotic Figure ◽  
Digital Display ◽  
Whole Slide Image ◽  
Wide Range ◽  
Slide Image ◽  
Conventional Microscopy ◽  
Mitotic Figures ◽  
Whole Slide Images

Validating digital pathology as substitute for conventional microscopy in diagnosis remains a priority to assure effectiveness. Intermodality concordance studies typically focus on achieving the same diagnosis by digital display of whole slide images and conventional microscopy. Assessment of discrete histological features in whole slide images, such as mitotic figures, has not been thoroughly evaluated in diagnostic practice. To further gauge the interchangeability of conventional microscopy with digital display for primary diagnosis, 12 pathologists examined 113 canine naturally occurring mucosal melanomas exhibiting a wide range of mitotic activity. Design reflected diverse diagnostic settings and investigated independent location, interpretation, and enumeration of mitotic figures. Intermodality agreement was assessed employing conventional microscopy (CM40×), and whole slide image specimens scanned at 20× (WSI20×) and at 40× (WSI40×) objective magnifications. An aggregate 1647 mitotic figure count observations were available from conventional microscopy and whole slide images for comparison. The intraobserver concordance rate of paired observations was 0.785 to 0.801; interobserver rate was 0.784 to 0.794. Correlation coefficients between the 2 digital modes, and as compared to conventional microscopy, were similar and suggest noninferiority among modalities, including whole slide image acquired at lower 20× resolution. As mitotic figure counts serve for prognostic grading of several tumor types, including melanoma, 6 of 8 pathologists retrospectively predicted survival prognosis using whole slide images, compared to 9 of 10 by conventional microscopy, a first evaluation of whole slide image for mitotic figure prognostic grading. This study demonstrated agreement of replicate reads obtained across conventional microscopy and whole slide images. Hence, quantifying mitotic figures served as surrogate histological feature with which to further credential the interchangeability of whole slide images for primary diagnosis.

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