SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

Abstract

This work tackles the automatic fine-grained slide quality assessment problem for digitized direct smears test using the Gram staining protocol. Automatic quality assessment can provide useful information for the pathologists and the whole digital pathology workflow. For instance, if the system found a slide to have a low staining quality, it could send a request to the automatic slide preparation system to remake the slide. If the system detects severe damage in the slides, it could notify the experts that manual microscope reading may be required. In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way. Specifically, the first step of our framework is to perform dense fine-grained region classification on the whole slide and calculate the region distribution histogram. Next, our framework will generate assessments of the slide quality from various perspectives: staining quality, information density, damage level and which regions are more valuable for subsequent high-magnification analysis. To make the information more accessible, we present our results in the form of a heat map and text summaries. Additionally, in order to stimulate research in this direction, we propose a novel dataset for slide quality assessment. Experiments show that the proposed framework outperforms recent related works.

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

“…Their approach can produce visually coherent marker-free WSIs while enhancing their quality (as assessed using PSNR, SSIM, and VIF IQA measures). In [72] 14 . From the patch-level artifact estimation statistics, they also provided a slide-level "usability" index that estimates wether the slide is appropriate for establishing a clinical diagnosis and an indication of the impact of artifacts on WSI quality.…”

“…This method provides a blur heat map as output but also provides a blur slide-level score as the percentage of the blurred surface of the WSI. Zhang et al [72] proposed using a CNN to assess the quality of WSIs stained with Gram staining. They considered a MobileNet CNN to estimate the staining quality of the slide tiles.…”

PathProfiler: Automated Quality Assessment of Retrospective Histopathology Whole-Slide Image Cohorts by Artificial Intelligence – A Case Study for Prostate Cancer Research

“…As a result of these challenges, the availability of quality assessment tools is currently limited, with only a small number developed specifically for histopathology slides [17][18][19][20][21][22][23] . To date, the available tools employ traditional hand-crafted features rather than learned ones 20,23 , or tend to be limited to identification of out-of-focus regions only [17][18][19][20] or identification of one artefact per image 21,22 . However, assessment for a combination of artefacts is more meaningful as in real life image artefacts are rarely limited to one feature such as poor staining or tissue folding, particularly with older glass slides.…”