scholarly journals Standardized Method for Defining a 1-mm2 Region of Interest for Calculation of Mitotic Rate on Melanoma Whole Slide Images

2021 ◽  
Author(s):  
Minhua Wang ◽  
Phyu P. Aung ◽  
Victor G. Prieto
Keyword(s):  
Hot Spot ◽  
Standard Procedure ◽  
Region Of Interest ◽  
Mitotic Rate ◽  
Routine Practice ◽  
Glass Slides ◽  
Slide Image ◽  
Standardized Method ◽  
Mitotic Figures ◽  
Whole Slide Images

Context.— Mitotic rate counting is essential in pathologic evaluations in melanoma. The American Joint Committee on Cancer recommends reporting the number of mitotic figures (MFs) in a 1-mm2 area encompassing the “hot spot.” There is currently no standard procedure for delineating a 1-mm2 region of interest for MF counting on a digital whole slide image (WSI) of melanoma. Objective.— To establish a standardized method to enclose a 1-mm2 region of interest for MF counting in melanoma based on WSIs and assess the method's effectiveness. Design.— Whole slide images were visualized using the ImageScope viewer (Aperio). Different monitors and viewing magnifications were explored and the annotation tools provided by ImageScope were evaluated. For validation, we compared mitotic rates obtained from WSIs with our method and those from glass slides with traditional microscopy with 30 melanoma cases. Results.— Of the monitors we examined, a 32-inch monitor with 3840 × 2160 resolution was optimal for counting MFs within a 1-mm2 region of interest in melanoma. When WSIs were viewed in the ImageScope viewer, ×10 to ×20 magnification during screening could efficiently locate a hot spot and ×20 to ×40 magnification during counting could accurately identify MFs. Fixed-shape annotations with 500 × 500-μm squares or circles can precisely and efficiently enclose a 1-mm2 region of interest. Our method on WSIs was able to produce a higher mitotic rate than with glass slides. Conclusions.— Whole slide images may be used to efficiently count MFs. We recommend fixed-shape annotation with 500 × 500-μm squares or circles for routine practice in counting MFs for melanoma.

scholarly journals Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1398
Author(s):  
Pushpanjali Gupta ◽  
Yenlin Huang ◽  
Prasan Kumar Sahoo ◽  
Jeng-Fu You ◽  
Sum-Fu Chiang ◽  
...  
Keyword(s):  
Interobserver Variability ◽  
Tissue Sample ◽  
Treatment Strategies ◽  
Region Of Interest ◽  
Final Diagnosis ◽  
Colon Tissue ◽  
Automated Method ◽  
Slide Image ◽  
Colon Tissue Sample ◽  
Whole Slide Images

Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue. In addition, there may be interobserver variability that might lead to conflict in the final diagnosis. As a result, there is a crucial need of developing an intelligent automated method that can learn from the patterns themselves and assist the pathologist in making a faster, accurate, and consistent decision for determining the normal and abnormal region in the colorectal tissues. Moreover, the intelligent method should be able to localize the abnormal region in the whole slide image (WSI), which will make it easier for the pathologists to focus on only the region of interest making the task of tissue examination faster and lesser time-consuming. As a result, artificial intelligence (AI)-based classification and localization models are proposed for determining and localizing the abnormal regions in WSI. The proposed models achieved F-score of 0.97, area under curve (AUC) 0.97 with pretrained Inception-v3 model, and F-score of 0.99 and AUC 0.99 with customized Inception-ResNet-v2 Type 5 (IR-v2 Type 5) model.

scholarly journals A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Christof A. Bertram ◽  
Marc Aubreville ◽  
Christian Marzahl ◽  
Andreas Maier ◽  
Robert Klopfleisch
Keyword(s):  
Mast Cell ◽  
Large Scale ◽  
Region Of Interest ◽  
Mitotic Figure ◽  
Low Grade ◽  
Detection Algorithms ◽  
Large Scale Dataset ◽  
Cutaneous Mast Cell ◽  
Mitotic Figures ◽  
Whole Slide Images

AbstractWe introduce a novel, large-scale dataset for microscopy cell annotations. The dataset includes 32 whole slide images (WSI) of canine cutaneous mast cell tumors, selected to include both low grade cases as well as high grade cases. The slides have been completely annotated for mitotic figures and we provide secondary annotations for neoplastic mast cells, inflammatory granulocytes, and mitotic figure look-alikes. Additionally to a blinded two-expert manual annotation with consensus, we provide an algorithm-aided dataset, where potentially missed mitotic figures were detected by a deep neural network and subsequently assessed by two human experts. We included 262,481 annotations in total, out of which 44,880 represent mitotic figures. For algorithmic validation, we used a customized RetinaNet approach, followed by a cell classification network. We find F1-Scores of 0.786 and 0.820 for the manually labelled and the algorithm-aided dataset, respectively. The dataset provides, for the first time, WSIs completely annotated for mitotic figures and thus enables assessment of mitosis detection algorithms on complete WSIs as well as region of interest detection algorithms.

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.

scholarly journals Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice

2021 ◽  
Author(s):  
Nehal M. Atallah ◽  
Michael S. Toss ◽  
Clare Verrill ◽  
Manuel Salto-Tellez ◽  
David Snead ◽  
...  
Keyword(s):  
Final Diagnosis ◽  
Normal Breast ◽  
Tissue Type ◽  
Routine Practice ◽  
File Size ◽  
Image File ◽  
Scanning Time ◽  
Glass Slides ◽  
Negative Linear Correlation ◽  
Slide Image

AbstractUsing digitalized whole slide images (WSI) in routine histopathology practice is a revolutionary technology. This study aims to assess the clinical impacts of WSI quality and representation of the corresponding glass slides. 40,160 breast WSIs were examined and compared with their corresponding glass slides. The presence, frequency, location, tissue type, and the clinical impacts of missing tissue were assessed. Scanning time, type of the specimens, time to WSIs implementation, and quality control (QC) measures were also considered. The frequency of missing tissue ranged from 2% to 19%. The area size of the missed tissue ranged from 1–70%. In most cases (>75%), the missing tissue area size was <10% and peripherally located. In all cases the missed tissue was fat with or without small entrapped normal breast parenchyma. No missing tissue was identified in WSIs of the core biopsy specimens. QC measures improved images quality and reduced WSI failure rates by seven-fold. A negative linear correlation between the frequency of missing tissue and both the scanning time and the image file size was observed (p < 0.05). None of the WSI with missing tissues resulted in a change in the final diagnosis. Missing tissue on breast WSI is observed but with variable frequency and little diagnostic consequence. Balancing between WSI quality and scanning time/image file size should be considered and pathology laboratories should undertake their own assessments of risk and provide the relevant mitigations with the appropriate level of caution.

scholarly journals A Digital Pathology Solution to Resolve the Tissue Floater Conundrum

2020 ◽  
Author(s):  
Liron Pantanowitz ◽  
Pamela Michelow ◽  
Scott Hazelhurst ◽  
Shivam Kalra ◽  
Charles Choi ◽  
...  
Keyword(s):  
Digital Pathology ◽  
Primary Diagnosis ◽  
Image Search ◽  
Molecular Tests ◽  
Tissue Identification ◽  
Digital Database ◽  
Glass Slides ◽  
Search Tool ◽  
Hematoxylin And Eosin ◽  
Whole Slide Images

Context.— Pathologists may encounter extraneous pieces of tissue (tissue floaters) on glass slides because of specimen cross-contamination. Troubleshooting this problem, including performing molecular tests for tissue identification if available, is time consuming and often does not satisfactorily resolve the problem. Objective.— To demonstrate the feasibility of using an image search tool to resolve the tissue floater conundrum. Design.— A glass slide was produced containing 2 separate hematoxylin and eosin (H&E)-stained tissue floaters. This fabricated slide was digitized along with the 2 slides containing the original tumors used to create these floaters. These slides were then embedded into a dataset of 2325 whole slide images comprising a wide variety of H&E stained diagnostic entities. Digital slides were broken up into patches and the patch features converted into barcodes for indexing and easy retrieval. A deep learning-based image search tool was employed to extract features from patches via barcodes, hence enabling image matching to each tissue floater. Results.— There was a very high likelihood of finding a correct tumor match for the queried tissue floater when searching the digital database. Search results repeatedly yielded a correct match within the top 3 retrieved images. The retrieval accuracy improved when greater proportions of the floater were selected. The time to run a search was completed within several milliseconds. Conclusions.— Using an image search tool offers pathologists an additional method to rapidly resolve the tissue floater conundrum, especially for those laboratories that have transitioned to going fully digital for primary diagnosis.

Proof of Concept: The Use of Whole-Slide Images (WSI) for Peer Review of Tissues on Routine Regulatory Toxicology Studies

2021 ◽  
pp. 019262332098325
Author(s):  
Alys E. Bradley ◽  
Maurice G. Cary ◽  
Kaori Isobe ◽  
Stuart Naylor ◽  
Stephen Drew
Keyword(s):  
Peer Review ◽  
Proof Of Concept ◽  
Regulatory Toxicology ◽  
Glass Slides ◽  
Microscopic Findings ◽  
Slight Discrepancy ◽  
Whole Slide Images ◽  
Diagnostic Discrepancies

This Proof of Concept (POC) study was to assess whether assessment of whole slide images (WSI) of the 2 target tissues for a contemporaneous peer review can elicit concordant results to the findings generated by the Study Pathologist from the glass slides. Well-focused WSI of liver and spleen from 4 groups of mice, that had previously been diagnosed to be the target tissues by an experienced veterinary toxicologic pathologist examining glass slides, were independently reviewed by 3 veterinary pathologists with varying experience in assessment of WSIs. Diagnostic discrepancies were then reviewed by an experienced adjudicating pathologist. Assessment of microscopic findings using WSI showed concordance with the glass slides, with only slight discrepancy in severity grades noted. None of the lesions recorded by the Study pathologist were “missed” and no lesions were added by the pathologists evaluating WSIs, thus demonstrating equivalence of the WSI to glass slides for this study.

scholarly journals Efficient pan-cancer whole-slide image classification and outlier detection using convolutional neural networks

2019 ◽  
Author(s):  
Seda Bilaloglu ◽  
Joyce Wu ◽  
Eduardo Fierro ◽  
Raul Delgado Sanchez ◽  
Paolo Santiago Ocampo ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Classification Problem ◽  
Classification Performance ◽  
Neoplastic Tissue ◽  
Multiple Tumor ◽  
Slide Image ◽  
Prediction Systems ◽  
Multi Class Classification ◽  
The Many ◽  
Whole Slide Images

AbstractVisual analysis of solid tissue mounted on glass slides is currently the primary method used by pathologists for determining the stage, type and subtypes of cancer. Although whole slide images are usually large (10s to 100s thousands pixels wide), an exhaustive though time-consuming assessment is necessary to reduce the risk of misdiagnosis. In an effort to address the many diagnostic challenges faced by trained experts, recent research has been focused on developing automatic prediction systems for this multi-class classification problem. Typically, complex convolutional neural network (CNN) architectures, such as Google’s Inception, are used to tackle this problem. Here, we introduce a greatly simplified CNN architecture, PathCNN, which allows for more efficient use of computational resources and better classification performance. Using this improved architecture, we trained simultaneously on whole-slide images from multiple tumor sites and corresponding non-neoplastic tissue. Dimensionality reduction analysis of the weights of the last layer of the network capture groups of images that faithfully represent the different types of cancer, highlighting at the same time differences in staining and capturing outliers, artifacts and misclassification errors. Our code is available online at: https://github.com/sedab/PathCNN.

Ovarian Frozen Section Diagnosis: Use of Whole-Slide Imaging Shows Excellent Correlation Between Virtual Slide and Original Interpretations in a Large Series of Cases

2010 ◽  
Vol 134 (7) ◽  
pp. 1020-1023 ◽  
Author(s):  
Margaret A. Fallon ◽  
David C. Wilbur ◽  
Manju Prasad
Keyword(s):  
Frozen Section ◽  
Virtual Slide ◽  
Granulosa Cell Tumor ◽  
Routine Practice ◽  
Corpora Lutea ◽  
Operating Characteristics ◽  
Frozen Section Diagnosis ◽  
Very High ◽  
Whole Slide Images

Abstract Context.—Whole-slide images (WSI) are a tool for remote interpretation, archiving, and teaching. Ovarian frozen sections (FS) are common and hence determination of the operating characteristics of the interpretation of these specimens using WSI is important. Objectives.—To test the reproducibility and accuracy of ovarian FS interpretation using WSI, as compared with routine analog interpretation, to understand the technology limits and unique interpretive pitfalls. Design.—A sequential series of ovarian FS slides, representative of routine practice, were converted to WSI. Whole-slide images were examined by 2 pathologists, masked to all prior results. Correlation characteristics among the WSI, the original, and the final interpretations were analyzed. Results.—A total of 52 cases, consisting of 71 FS slides, were included; 34 cases (65%) were benign, and 18 cases (35%) were malignant, borderline, and of uncertain potential (9 [17%], 7 [13%], and 2 [4%] of 52 cases, respectively). The correlation between WSI and FS interpretations was 96% (50 of 52) for each pathologist for benign versus malignant, borderline, and uncertain entities. Each pathologist undercalled 2 borderline malignant cases (4%) as benign cysts on WSI. There were no overcalls of benign cases. Specific issues within the benign and malignant groups involved endometriosis versus hemorrhagic corpora lutea, and granulosa cell tumor versus carcinoma, respectively. Conclusions.—The correlation between original FS and WSI interpretations was very high. The few discordant cases represent recognized differential diagnostic issues. Ability to examine gross pathology and real-time consultation with surgeons might be expected to improve performance. Ovarian FS diagnosis by WSI is accurate and reproducible, and thus, remote interpretation, teaching, and digital archiving of ovarian FS specimens by this method can be reliable.

Registration accuracy between whole slide images and glass slides in eeDAP workflow

2018 ◽  
Author(s):  
Qi Gong ◽  
Benjamin P. Berman ◽  
Marios A. Gavrielides ◽  
Brandon D. Gallas
Keyword(s):  
Registration Accuracy ◽  
Glass Slides ◽  
Whole Slide Images
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