An alternative reference space for H&amp;E color normalization

Zarella, Mark D.; Yeoh, Chan; Breen, David E.; Garcia, Fernando U.

doi:10.1371/journal.pone.0174489

Cited by 36 publications

(23 citation statements)

References 28 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, color normalization for such algorithms improves their overall performance. A number of color normalization approaches have been developed that utilize intensity thresholds, histogram normalization, stain separation, color deconvolution, structurebased color classification, and generative adversarial networks [36][37][38][39][40][41]. However, it is important to evaluate and prevent the image distortion that can result from these techniques.…”

Section: Basics Of Image Analysis: Cellular Analysis and Color Normalmentioning

confidence: 99%

Introduction to digital pathology and computer-aided pathology

Nam

Chong

Jung

et al. 2020

J Pathol Transl Med

View full text Add to dashboard Cite

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/ by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. pISSN 2383-7837 eISSN 2383-7845Digital pathology (DP) is now one of the biggest issues facing the field of pathology. DP is a remarkable innovation that changes the paradigm of microscope-based pathology, which has existed for over 100 years. DP alters the way diagnostic tools represent pathologic images from the microscope to the computer screen and has changed storage media from glass slides to digitalized image files. Digitalized pathologic images stored in computer servers or cloud systems can be transmitted over the Internet, thus changing the temporal and spatial domain of pathologic diagnosis. In addition, machine learning allows software assisting in diagnosis to be developed and applied more actively and effectively.This review describes various concepts related to DP and computer-aided pathologic diagnosis (CAPD), current applications of DP, and various issues related to the implementation of DP. It also briefly introduces the development of computer-aided diagnostic tools and their limitations. DIGITAL PATHOLOGY AND COMPUTER-AIDED PATHOLOGYDP, which initially delineated the process of digitizing whole slide images (WSIs) using advanced slide scanning technology, is now a generic term that includes artificial intelligence (AI)based approaches for detection, segmentation, diagnosis, and analysis of digitalized images [1]. WSI indicates digital representation of an entire histopathologic glass slide at microscopic resolution [2]. Over the last two decades, WSI technology has evolved to encompass relatively high resolution, increased scanner capacity, faster scan speed, smaller image file sizes, and commercialization. The development of appropriate image management systems (IMS) and a seamless interface connection between existing hospital systems such as electronic medical records (EMR), picture archiving communication systems (PACS), and laboratory information systems (LIS) (also referred to as the pathol-Introduction to digital pathology and computer-aided pathology Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for many pathologists to understand the engineering and mathematics concepts involved in DP. Computer-aided pathology (CAP) aids pathologists in diagnosis. However, some consider CAP a threat to the existence of pathologists and are skeptical of its clinical utility. Implementation of DP is very burdensome for pathologists because technical factors, impact on workflow, and information technology infrastructure must be considered. In this paper, various terms related to DP and computer-aided pathologic diagnosis are defined, current applications of DP are discussed, and various issues related to implementation of DP are outlined. The developmen...

show abstract

Section: Basics Of Image Analysis: Cellular Analysis and Color Normalmentioning

confidence: 99%

Introduction to digital pathology and computer-aided pathology

Nam

Chong

Jung

et al. 2020

J Pathol Transl Med

View full text Add to dashboard Cite

show abstract

“…Several studies have devoted their efforts to developing robust color normalization methods for H&E stained histopathology images. Intensity thresholding [25], histogram normalization [26], stain separation [27], color deconvolution [28], and combining spatial information with color information [29] are representative normalization methods. The previously proposed DCGMM obtains state-of-the-art color normalization performance on H&E stained histopathology images with the large stain variations [24].…”

Section: Color Normalizationmentioning

confidence: 99%

An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images

2019

View full text Add to dashboard Cite

Background: Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. Results: To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. Conclusions: We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. Our method also can facilitate downstream nuclei morphological analyses as it provides high-quality features extracted from histopathology images.

show abstract

“…A combination of methods were used to propagate elastic modulus measurements throughout the sample. The tiled high-resolution H&E images were first segmented into structural information using a technique based on [11]. First, 18 color images (each just over 3 megapixels) from both cancerous and benign tissue samples were manually selected that contained all three relevant structure types: cell nuclei and surrounding cytoplasm, stroma, and lumen or background.…”

Section: Stiffness Propagationmentioning

confidence: 99%

Whole-Sample Mapping of Cancerous and Benign Tissue Properties

Neary-Zajiczek¹,

Essmann²,

Clancy³

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Structural and mechanical differences between cancerous and healthy tissue give rise to variations in macroscopic properties such as visual appearance and elastic modulus that show promise as signatures for early cancer detection. Atomic force microscopy (AFM) has been used to measure significant differences in stiffness between cancerous and healthy cells owing to its high force sensitivity and spatial resolution, however due to absorption and scattering of light, it is often challenging to accurately locate where AFM measurements have been made on a bulk tissue sample. In this paper we describe an image registration method that localizes AFM elastic stiffness measurements with high-resolution images of haematoxylin and eosin (H&E)-stained tissue to within ±1.5 µm. Color RGB images are segmented into three structure types (lumen, cells and stroma) by a neural network classifier trained on ground-truth pixel data obtained through k-means clustering in HSV color space. Using the localized stiffness maps and corresponding structural information, a whole-sample stiffness map is generated with a region matching and interpolation algorithm that associates similar structures with measured stiffness values. We present results showing significant differences in stiffness between healthy and cancerous liver tissue and discuss potential applications of this technique.

show abstract

An alternative reference space for H&E color normalization

Cited by 36 publications

References 28 publications

Introduction to digital pathology and computer-aided pathology

Introduction to digital pathology and computer-aided pathology

An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images

Whole-Sample Mapping of Cancerous and Benign Tissue Properties

Contact Info

Product

Resources

About