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Pathology imaging informatics for quantitative analysis of whole-slide images.

Kothari S, Phan JH, Stokes TH, Wang MD - J Am Med Inform Assoc (2013)

Bottom Line: With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities.Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making.We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.

ABSTRACT

Objectives: With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities.

Target audience: This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods.

Scope: First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.

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Related in: MedlinePlus

Normalization of color batch effects in ovarian samples provided by the NIH Cancer Genome Atlas.
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AMIAJNL2012001540F3: Normalization of color batch effects in ovarian samples provided by the NIH Cancer Genome Atlas.

Mentions: Differences in slide preparation, microscope, and digitizing device between two batches of data may lead to differences in image properties between the two batches. These differences, called batch effects, can bias the performance estimates of predictive models. Histopathological images often suffer from color and scale batch effects. Color batch effects can be addressed by normalizing the color of an image to a reference image18–20 or by converting the image to a color space (eg, CIELAB) that is not affected by color batch effects.21–23Figure 3 illustrates results for normalizing the color map of two ovarian samples (obtained from TCGA) using color-map quantile normalization.18 Color normalization can be performed either at the pixel level using a single model for a complete image18 or at the stain level using a different model for each stain.20 Pixel-level normalization is affected by differences in morphology between the reference and test images while stain-level normalization is affected by the accuracy of stain segmentation. Unlike color batch effects, which affect only color properties of an image, scale batch effects can affect a variety of image features such as object size, topology, and texture. However, scale batch effects may be difficult to detect or correct because biological factors such as cancer grade or subtype may induce changes in scale. Such batch effects may be detected by examining the differences in distribution of image features between batches. For example, Kothari et al24 detected and proposed a method for correcting scale batch effects by examining the distributions of nuclear areas. Studies suggest that batch effects, if left uncorrected, can severely reduce the performance of genomic prediction models.2526 Even though preliminary investigations suggest that batch effects are present in histopathological images, most researchers validate their diagnostic models on a single image dataset collected during a single experimental set-up. For clinical application of these systems, it is essential to validate diagnostic models on multiple datasets and to develop effective batch-effect removal methods.


Pathology imaging informatics for quantitative analysis of whole-slide images.

Kothari S, Phan JH, Stokes TH, Wang MD - J Am Med Inform Assoc (2013)

Normalization of color batch effects in ovarian samples provided by the NIH Cancer Genome Atlas.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3822114&req=5

AMIAJNL2012001540F3: Normalization of color batch effects in ovarian samples provided by the NIH Cancer Genome Atlas.
Mentions: Differences in slide preparation, microscope, and digitizing device between two batches of data may lead to differences in image properties between the two batches. These differences, called batch effects, can bias the performance estimates of predictive models. Histopathological images often suffer from color and scale batch effects. Color batch effects can be addressed by normalizing the color of an image to a reference image18–20 or by converting the image to a color space (eg, CIELAB) that is not affected by color batch effects.21–23Figure 3 illustrates results for normalizing the color map of two ovarian samples (obtained from TCGA) using color-map quantile normalization.18 Color normalization can be performed either at the pixel level using a single model for a complete image18 or at the stain level using a different model for each stain.20 Pixel-level normalization is affected by differences in morphology between the reference and test images while stain-level normalization is affected by the accuracy of stain segmentation. Unlike color batch effects, which affect only color properties of an image, scale batch effects can affect a variety of image features such as object size, topology, and texture. However, scale batch effects may be difficult to detect or correct because biological factors such as cancer grade or subtype may induce changes in scale. Such batch effects may be detected by examining the differences in distribution of image features between batches. For example, Kothari et al24 detected and proposed a method for correcting scale batch effects by examining the distributions of nuclear areas. Studies suggest that batch effects, if left uncorrected, can severely reduce the performance of genomic prediction models.2526 Even though preliminary investigations suggest that batch effects are present in histopathological images, most researchers validate their diagnostic models on a single image dataset collected during a single experimental set-up. For clinical application of these systems, it is essential to validate diagnostic models on multiple datasets and to develop effective batch-effect removal methods.

Bottom Line: With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities.Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making.We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.

ABSTRACT

Objectives: With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities.

Target audience: This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods.

Scope: First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.

Show MeSH
Related in: MedlinePlus