scholarly journals Superpixel Image Segmentation of the Tumoral Microenvironment in Colorectal Cancer: Frontiers Beyond the Microscope

2021 ◽  
Author(s):  
Sean Hacking ◽  
Dongling Wu ◽  
Claudine Alexis ◽  
Mansoor Nasim
Keyword(s):  
Colorectal Cancer ◽  
Clinical Trials ◽  
Image Analysis ◽  
Quality Assurance ◽  
Image Segmentation ◽  
Digital Image Analysis ◽  
Tumor Stage ◽  
Gi Tract ◽  
Common Malignancy ◽  
Whole Slide Images

Abstract Colorectal cancer (CRC) is the most common malignancy of the gastrointestinal (GI) tract and accounts for 9% of all cancers. The stroma and the tumoral microenvironment represent brave new frontiers for patients with colorectal cancer. Here we demonstrate novel superpixel image segmentation (SIS) techniques for whole slide images (WSI) to unravel this biology. Findings of significance include the association of low proportionated stromal area (PSA), high immature stromal percentage (ISP) and high myxoid stromal ratio (MSR) with worse prognostic outcomes in our CRC patients. Overall, stromal markers outperformed all others at predicting clinical outcomes. In particular, MSR may be able to prognosticate patients independent of tumor stage and may be the most optimal way to effectively prognosticate CRC patients which circumvents the need for more extensive deep learning (DL) based computational profiling. Approaches demonstrated here can be performed by a trained pathologist and very easily recorded during synoptic cancer reporting with appropriate quality assurance. Future well-designed, robust clinical trials will have the ultimate say in determining whether digital image analysis and superpixel image segmentation can better tailor the need for adjuvant therapy in patients with colorectal cancer.

scholarly journals Superpixel Image Segmentation of the Tumoral Microenvironment in Colorectal Cancer: Frontiers Beyond the Microscope

2021 ◽  
Author(s):  
Sean Hacking ◽  
Dongling Wu ◽  
Claudine Alexis ◽  
Mansoor Nasim
Keyword(s):  
Colorectal Cancer ◽  
Clinical Trials ◽  
Quality Assurance ◽  
Image Segmentation ◽  
Digital Image Analysis ◽  
Tumor Stage ◽  
Gi Tract ◽  
Myxoid Stroma ◽  
Common Malignancy ◽  
Whole Slide Images

Abstract Colorectal cancer (CRC) is the most common malignancy of the gastrointestinal (GI) tract and accounts for 9% of all cancers. The stroma and the tumoral microenvironment represent brave new frontiers for patients with colorectal cancer. Here we demonstrate novel superpixel image segmentation (SIS) techniques for whole slide images (WSI) to unravel this biology. Findings of significance include the association of low proportionated stromal area (PSA), high immature stromal percentage (ISP) and high myxoid stroma ratio (MSR) with worse prognostic outcomes in our CRC patients. Overall, stromal markers outperformed all others at predicting clinical outcomes. In particular, MSR may be able to prognosticate patients independent of tumor stage and may be the most optimal way to effectively prognosticate CRC patients which circumvents the need for more extensive deep learning (DL) based computational profiling. Approaches demonstrated here can be performed by a trained pathologist and very easily recorded during synoptic cancer reporting with appropriate quality assurance. Future well-designed, robust clinical trials will have the ultimate say in determining whether digital image analysis and superpixel image segmentation can better tailor the need for adjuvant therapy in patients with colorectal cancer.

scholarly journals Superpixel Image Segmentation of VISTA Expression in Colorectal Cancer: Implications for Immunotherapy

2020 ◽  
Author(s):  
Dongling Wu ◽  
Sean Hacking ◽  
Taisia Vitkovski ◽  
Mansoor Nasim
Keyword(s):  
Colorectal Cancer ◽  
Clinical Trials ◽  
Image Segmentation ◽  
T Cell Activation ◽  
Cell Activation ◽  
Disease Free Survival ◽  
Tumor Infiltrating Lymphocytes ◽  
Tumor Stage ◽  
Infiltrating Lymphocytes ◽  
Patient Enrollment

Abstract Colorectal cancer is an overall bad player and accounts for 9% of all cancers. Today, advancements in immune checkpoint inhibition has provided therapeutics for many, but not all cancer patients. This issue is in part due to the tumoral microenvironment; which plays a significant role in determining response to immune check point therapeutics. This study serves as the first to evaluate a potent inhibitory checkpoint: V-domain immunoglobulin suppressor of T cell activation (VISTA) and its role in CRC. This was evaluated with both conventional light microscope and superpixel image segmentation. Here we found VISTA expression to be associated with low tumor budding, lower tumor stage, high tumor infiltrating lymphocytes, mature stromal differentiation, BRAF mutation status and better disease-free survival in colorectal cancer. When comparing methodologies; superpixel image segmentation better stratified patients into prognostic groups. Anti-VISTA clinical trials are now open and recruiting for patient enrollment for patients with certain advanced solid tumors. Considering raised VISTA expression is associated with improved survival for patients with colorectal cancer: careful, well-designed and robust clinical trials should be pursued in this cancer subtype.

scholarly journals Superpixel image segmentation of VISTA expression in colorectal cancer and its relationship to the tumoral microenvironment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dongling Wu ◽  
Sean Hacking ◽  
Taisia Vitkovski ◽  
Mansoor Nasim
Keyword(s):  
Colorectal Cancer ◽  
Image Segmentation ◽  
T Cell Activation ◽  
Cell Activation ◽  
Disease Free Survival ◽  
Tumor Stage ◽  
The United States ◽  
Mutational Status ◽  
Microsatellite Stability

AbstractColorectal cancer (CRC) is the third most common cause of cancer related death in the United States (Jasperson et al. in Gastroenterology 138:2044–2058, 10.1053/j.gastro.2010.01.054, 2010). Many studies have explored prognostic factors in CRC. Today, much focus has been placed on the tumor microenvironment, including different immune cells and the extracellular matrix (ECM). The present study aims to evaluate the role of V-domain immunoglobulin suppressor of T cell activation (VISTA). We utilized QuPath for whole slides image analysis, performing superpixel image segmentation (SIS) on a 226 patient-cohort. High VISTA expression correlated with better disease-free survival (DFS), high tumor infiltrative lymphocyte, microsatellite instability, BRAF mutational status as well as lower tumor stage. High VISTA expression was also associated with mature stromal differentiation (SD). When cohorts were separated based on SD and MMR, only patients with immature SD and microsatellite stability were found to correlate VISTA expression with DFS. Considering raised VISTA expression is associated with improved survival, TILs, mature SD, and MMR in CRC; careful, well-designed clinical trials should be pursued which incorporate the underlying tumoral microenvironment.

Digital image analysis of pan-cytokeratin stained tumor slides for evaluation of tumor budding in pT1/pT2 colorectal cancer: Results of a feasibility study

2018 ◽  
Vol 214 (9) ◽  
pp. 1273-1281 ◽  
Author(s):  
Rikke Karlin Jepsen ◽  
Louise Laurberg Klarskov ◽  
Michael Friis Lippert ◽  
Guy Wayne Novotny ◽  
Tine Plato Hansen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Image Analysis ◽  
Feasibility Study ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Tumor Budding

Laboratory validation studies in Ki-67 digital image analysis of breast carcinoma: a pathway to routine quality assurance

Pathology ◽  
2019 ◽  
Vol 51 (3) ◽  
pp. 246-252 ◽  
Author(s):  
Morgan Wang ◽  
Sally McLaren ◽  
Roopaa Jeyathevan ◽  
Benjamin Michael Allanson ◽  
Amanda Ireland ◽  
...  
Keyword(s):  
Image Analysis ◽  
Quality Assurance ◽  
Breast Carcinoma ◽  
Digital Image ◽  
Validation Studies ◽  
Digital Image Analysis ◽  
Ki 67

scholarly journals Estimation of Blast Severity on Rye and Triticale Spikes by Digital Image Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
João Leodato Nunes Maciel ◽  
Alfredo do Nascimento Junior ◽  
Cristina Boaretto
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Digital Image Analysis ◽  
Linear Regression Analysis ◽  
Reference Method ◽  
Coefficient Of Determination ◽  
Breeding Programs ◽  
True Value ◽  
Mouse Cursor ◽  
Selection Of

In Brazil, more efficient methods are a necessity for evaluating blast severity on spikes in the breeding programs of rye, triticale, wheat, and barley. The objective of this work was to determine the feasibility of assessing blast severity based on the analysis of digital images of symptomatic rye and triticale spikes. Triticale and rye genotypes were grown to anthesis in pots and were then inoculated with a mixture ofMagnaporthe oryzaeisolates. Blast severity on the spikes was evaluated visually and after that the spikes were detached and photographed. Blast severity was determined using the program ImageJ to analyze the obtained images. Two methods of image analysis were used: selection of symptomatic areas using a mouse cursor (SCU) and selection of symptomatic areas using image segmentation (SIS). The SCU method was considered the standard reference method for determining the true value of blast severity on spikes. An analysis of variance did not determine any difference among the evaluation methods. The coefficient of determination (R2) obtained from a linear regression analysis between the variables SIS and SCU was 0.615. The obtained data indicate that the evaluation of blast severity on spikes based on image segmentation is feasible and reliable.

scholarly journals An Algorithm for Determining the Location of a Segment on a Segmented Image

2021 ◽  
pp. 98-105
Author(s):  
Otar Tavdishvili ◽  
◽  
Zurab Alimbarashvili ◽  
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Image Plane ◽  
Segmentation Algorithm ◽  
Connected Components ◽  
Original Algorithm ◽  
Segmented Image ◽  
Number Of Segments

The task of digital image analysis after image segmentation requires the ability to operate independently with each extracted segment (object), determine the total number of segments, and define the location of the segments on the image plane. This requires knowledge of the coordinates of each extracted segment on the image. This involves defining the coordinates of the pixels that make up the segment. For this purpose, an original algorithm was developed, which during the process of implementation of early developed non-parametric segmentation algorithm extracts the connected components (segments) on the image and determines the location of the each segment on the image based on the values of indices 𝑖 and 𝑗 (coordinates) of its pixels.

scholarly journals Validation of a Muscle-Specific Tissue Image Analysis Tool for Quantitative Assessment of Dystrophin Staining in Frozen Muscle Biopsies

2018 ◽  
Vol 143 (2) ◽  
pp. 197-205 ◽  
Author(s):  
Famke Aeffner ◽  
Crystal Faelan ◽  
Steven A. Moore ◽  
Alexander Moody ◽  
Joshua C. Black ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Image Analysis ◽  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Analysis Tool ◽  
Protein Loss ◽  
Dystrophin Expression ◽  
Analysis Workflow

Context.— Duchenne muscular dystrophy is a rare, progressive, and fatal neuromuscular disease caused by dystrophin protein loss. Common investigational treatment approaches aim at increasing dystrophin expression in diseased muscle. Some clinical trials include assessments of novel dystrophin production as a surrogate biomarker of efficacy, which may predict a clinical benefit from treatment. Objectives.— To establish an immunofluorescent scanning and digital image analysis workflow that provides an objective approach for staining intensity assessment of the immunofluorescence dystrophin labeling and determination of the percentage of biomarker-positive fibers in muscle cryosections. Design.— Optimal and repeatable digital image capture was achieved by a rigorously qualified fluorescent scanning process. After scanning qualification, the MuscleMap (Flagship Biosciences, Westminster, Colorado) algorithm was validated by comparing high-power microscopic field total and dystrophin-positive fiber counts obtained by trained pathologists to data derived by MuscleMap. Next, the algorithm was tested on whole-slide images of immunofluorescent-labeled muscle sections from Duchenne muscular dystrophy, Becker muscular dystrophy, and control patients. Results.— When used under the guidance of a trained pathologist, the digital image analysis tool met predefined validation criteria and demonstrated functional and statistical equivalence with manual assessment. This work is the first, to our knowledge, to qualify and validate immunofluorescent scanning and digital tissue image-analysis workflow, respectively, with the rigor required to support the clinical trial environments. Conclusions.— MuscleMap enables analysis of all fibers within an entire muscle biopsy section and provides data on a fiber-by-fiber basis. This will allow future clinical trials to objectively investigate myofibers' dystrophin expression at a greater level of consistency and detail.

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