Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Abstract

With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital revolution. Simultaneously, with the development of image analysis algorithms based on artificial intelligence tools, the application of computerized WSI analysis can now be expected. However, transferring such tools into clinical practice is very challenging as they must deal with many artifacts that can occur during sample preparation and digitization. Therefore, the quality of WSIs is of prime importance, and we propose a review of the state-of-the-art of computational approaches for quality control. In particular, we focus on WSI quality issues related to the presence of sample preparation artifacts, compression artifacts, color variations, and out-of-focus areas. An analysis of the monthly WSI clinical routine in a cytological laboratory confirms the importance of implementing quality control measures. Given this observation, we draw perspectives on how a computational quality process can be included in a computational pathology diagnosis pipeline.

Wavelet scattering‐ and object detection‐based computer vision for identifying dengue from peripheral blood microscopy

“…Digitization of whole-slide pathological images has facilitated the use of artificial intelligence-enabled tools for medical diagnosis. [12][13][14] In particular, computer visionbased approaches have been harnessed for object detection, segmentation, and classification tasks involving digitized medical images. 12,15 Both machine learning and deep learning approaches can be deployed in automated digital pathology-based computer vision applications.…”

“…Digitization of whole‐slide pathological images has facilitated the use of artificial intelligence‐enabled tools for medical diagnosis 12–14 . In particular, computer vision‐based approaches have been harnessed for object detection, segmentation, and classification tasks involving digitized medical images 12,15 .…”

Robust autofocus method based on patterned active illumination and image cross-correlation analysis

“…However, the performance of an intelligent diagnosis system is highly dependent on related to the quality of WSI images [ 5 ]. It has been reported that 97% of the errors in AI-aided diagnosis results are caused by poor image quality [ 6 ]. One of the main factors leading to the degradation of image quality is that the sample is out of focus during imaging [ 7 ].…”

Computational pathology: A survey review and the way forward

“…A second group of works focus on clinical barriers for AI integration discussing specific certifications and regulations required for the development of medical devices under clinical settings. 44 , 45 , 46 , 47 , 48 , 49 Lastly, the final group focuses on both the design and the integration of AI tools with clinical applications. 12 , 13 , 14 , 29 , 50 , 51 , 52 , 53 , 54 , 55 , 56 These works speak to both the computer vision and pathology communities in developing machine learning (ML) models that can satisfy clinical use cases.…”