Automating proliferation rate estimation from Ki-67 histology images

Al-Lahham, Heba Z.; Alomari, Raja’ S.; Hiary, Hazem; Chaudhary, Vipin

doi:10.1117/12.911009

Cited by 12 publications

(13 citation statements)

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“…Our most senior pathologist (coauthor) spent over 10 hours for scoring the 100 cases which means an average of 6 minutes per case. Moreover, we run our proposed automated PRE system proposed in [7] over the same 100 images and recorded the automated scoring for both the blue and the brown nuclei.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Specifically for breast cancer PRE [9] [10], we find that pathologists provide variable ground truth estimations which make it hard to evaluate any automated PRE estimated technique. Many automated PRE techniques have been proposed in the literature and we recently proposed our technique in [7], an exhaustive review of the techniques as well as a detailed description of our techniques are presented in [7]. In this paper, we provide the necessary statistical study for the inter pathologists variability.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, we study the manual vs automated [7] PRE variability. We study three statistical significance measures: correlation coefficient, T-Test, and Ch-Square test.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, we study the statistical variability between the four manual ground truth and our automated technique. In [7], we compared the automated results with one expert pathologist and a student trained by a pathologist. In this paper, we run our statistical study to include for expert pathologists and one automated technique.…”

Section: Methodsmentioning

confidence: 99%

“…Our expert pathologist requires over 10 hours estimating the proliferation rate for our dataset containing 100 images. Many authors target automation of PRE including our recent work [7]. However, one major concern was not investigated in all these efforts which was the inter-variability between the expert pathologists [8].…”

Section: Introductionmentioning

confidence: 99%

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Automatic and Manual Proliferation Rate Estimation from Digital Pathology Images

Rajab¹,

Al-Lahham²,

Alomari³

et al. 2015

JSEA

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Digital pathology is a major revolution in pathology and is changing the clinical routine for pathologists. We work on providing a computer aided diagnosis system that automatically and robustly provides the pathologist with a second opinion for many diagnosis tasks. However, interobserver variability prevents thorough validation of any proposed technique for any specific problems. In this work, we study the variability and reliability of proliferation rate estimation from digital pathology images for breast cancer proliferation rate estimation. We also study the robustness of our recently proposed method CAD system for PRE estimation. Three statistical significance tests showed that our automated CAD system was as reliable as the expert pathologist in both brown and blue nuclei estimation on a dataset of 100 images.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Moreover, we study the manual vs automated [7] PRE variability. We study three statistical significance measures: correlation coefficient, T-Test, and Ch-Square test.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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