[Paper] Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

Abstract

Previously, a referenceless quality evaluation method (RQM) was proposed by Hashimoto et al. to evaluate the quality of the scanned image 7) for the purpose of Abstract A whole slide imaging (WSI) scanner scans pathological-specimens to produce digital images for monitor-based diagnosis and analysis. However, the image quality is sometimes insufficient due to focus-error or noise, in which case the slide needs to be rescanned. In previous work, a referenceless quality evaluation technique was proposed, but some artifacts (i.e. tissue-fold, air-bubble) were detected as false positives. Those artifacts need to be ignored in determining whether rescanning is necessary or not, because they are not caused in the scanning but slide preparation stage. This paper proposes a method for a more practical system to assess WSI quality by distinguishing the origins of quality degradation; the focus-error or noise caused by the scanner and the artifact occurred in the slide preparation. In the method, a support vector machine detects artifacts first, and then quality is evaluated excluding artifact regions. The effectiveness of the proposed system has been experimentally demonstrated.

The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

“…The method presented by Hashimoto et al [51] uses a combination of image sharpness and noise measurements to derive a linear regression model in order to provide a quality metric. This work was extended by Shakhawat et al [48] to distinguish whether the low quality of an image was caused by scanning or other artifacts. Ameisen et al [74] proposed a set of QC metrics using blurriness, color separation, brightness and contrast assessments to evaluate different scanners.…”

“…The presence of these artifacts directly affects the performance of the CPATH systems; thus, it is crucial to detect WSIs or patches containing artifacts [48]. Having provided a general classification of the different artifacts that can occur during the WSI acquisition process, we will now focus on further detailing scan-level artifacts, blur and the tissue-level artifacts, folded tissue, blood, and damaged areas because those artifacts can have a major impact on WSI analysis and have been somewhat explored in the literature.…”

“…The use of color-based features might be affected by varying staining protocols. To overcome this issue, Shakhawat et al [48] proposed the use of data-driven features trained with heterogeneous datasets. They explored the use of GLCM to…”

“…The objective of a QC is to quickly identify faulty WSI containing significantly distorted features in order to discard or re-scan them before carrying out any further analysis [17]. QC approaches try to evaluate each WSI and provide a quality metric, usually by looking at lower magnifications in the multi-resolution pyramid [48], [49], [74]. The overall quality metrics are designed according to what pathologists consider to be the most relevant features and often include sharpness of the image, amount of artifacts or noise, and contrast metrics [49].…”

“…Current approaches often rely on low magnification analysis to discard complete WSIs. It is a well-known issue that QC approaches need to be extended to higher magnification in order to effectively detect some of the artifacts [48]. This, however, would require new computational efficient techniques to be implemented that can deal with the massive size of the WSIs.…”