Multi-resolution tile-based follicle detection using color and textural information of follicular lymphoma IHC slides

Han, Jilong; Shin, Dmitriy; Arthur, Gerald L.; Shyu, Chi‐Ren

doi:10.1109/bibmw.2010.5703949

Cited by 5 publications

(7 citation statements)

References 4 publications

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“…Shariff et al provide a review of the problems faced in the domain of IHC image analysis along with solutions and techniques used in the area (93). Subsequently several image processing and machine learning based approaches have been proposed providing different levels of accuracies (94–96). Then work of Lejeune et al (97) is significant as they perform automated analysis for quantification of proteins for different nuclear (ki67, p53), cytoplasmic (TIA-1, CD68) and membrane markers (CD4, CD8, CD56, HLA-Dr).…”

Section: Selected Applications Of Ai In Pathologymentioning

confidence: 99%

Translational AI and Deep Learning in Diagnostic Pathology

et al. 2019

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There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.

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Section: Selected Applications Of Ai In Pathologymentioning

confidence: 99%

Translational AI and Deep Learning in Diagnostic Pathology

et al. 2019

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“…In [49,110,113,114], L * u * v * color space is used on LHIA for L * u * v * color space are uniform in perception. In [49,105,107,110,111,[115][116][117][118][119][120][121][122][123][124][125], HSV color space is used on LHIA for HSV separating the intensity from the color information. In [103], HSI color space is used on LHIA for HSI is hue based color space similar to HSV.…”

Section: Color-based Preprocessing Techniquesmentioning

confidence: 99%

“…10. In [64,110,118,130,131,[136][137][138][144][145][146], Gaussian filter is used to remove noise and small details, convolve input images, smooth images, homogenize color intensity, adjust contrast. In [106], a matching filter is used for removing noise, flattening background and enhancing the contours of the follicle region to homogenize images.…”

Section: Filter-based Preprocessing Techniquesmentioning

confidence: 99%

“…The purposes of the step include decreasing space complexity for feature extraction, the local features extracted by blocks can improve the results than the global features extracted without blocks and different features useful for classification in different regions of the image can be extracted. In [96,118,120,132,135,150,152,[158][159][160][161][162][163], the original images are cropped into smaller patches.…”

Section: Other Preprocessing Techniquesmentioning

confidence: 99%

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What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

Li¹,

Chen²,

Li³

et al. 2022

Preprint

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In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide doctors with diagnostic references. In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis. This paper reviews the applications of image processing technology based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection. Finally, the current methods are analyzed, some more potential methods are proposed, and further prospects are made.

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“…However, the work in underway to use advanced WSI image processing algorithms to aid WSI annotation. For instance, we have been developing novel methods for follicle and nucleus detection on WSI[ 25 26 ] and plan on incorporating these algorithms into the PathEdEx ecosystem.…”

Section: Ethodsmentioning

confidence: 99%

PathEdEx – Uncovering High-explanatory Visual Diagnostics Heuristics Using Digital Pathology and Multiscale Gaze Data

Shin

Kovalenko

Ersoy

et al. 2017

Journal of Pathology Informatics

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Background:Visual heuristics of pathology diagnosis is a largely unexplored area where reported studies only provided a qualitative insight into the subject. Uncovering and quantifying pathology visual and nonvisual diagnostic patterns have great potential to improve clinical outcomes and avoid diagnostic pitfalls.Methods:Here, we present PathEdEx, an informatics computational framework that incorporates whole-slide digital pathology imaging with multiscale gaze-tracking technology to create web-based interactive pathology educational atlases and to datamine visual and nonvisual diagnostic heuristics.Results:We demonstrate the capabilities of PathEdEx for mining visual and nonvisual diagnostic heuristics using the first PathEdEx volume of a hematopathology atlas. We conducted a quantitative study on the time dynamics of zooming and panning operations utilized by experts and novices to come to the correct diagnosis. We then performed association rule mining to determine sets of diagnostic factors that consistently result in a correct diagnosis, and studied differences in diagnostic strategies across different levels of pathology expertise using Markov chain (MC) modeling and MC Monte Carlo simulations. To perform these studies, we translated raw gaze points to high-explanatory semantic labels that represent pathology diagnostic clues. Therefore, the outcome of these studies is readily transformed into narrative descriptors for direct use in pathology education and practice.Conclusion:PathEdEx framework can be used to capture best practices of pathology visual and nonvisual diagnostic heuristics that can be passed over to the next generation of pathologists and have potential to streamline implementation of precision diagnostics in precision medicine settings.

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Multi-resolution tile-based follicle detection using color and textural information of follicular lymphoma IHC slides

Cited by 5 publications

References 4 publications

Translational AI and Deep Learning in Diagnostic Pathology

Translational AI and Deep Learning in Diagnostic Pathology

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

PathEdEx – Uncovering High-explanatory Visual Diagnostics Heuristics Using Digital Pathology and Multiscale Gaze Data

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