The system shows potential to be used in the AI-assisted future of the workflow in clinical practice. The average inference time per slide is ~6 minutes and can be done in parallel to reduce inference time. The generalization of both networks may be improved with more annotations.
#ARTIFACT IMAGES MANUAL#
We developed an AI system consisting of an artifact segmentation network to make other algorithms more robust against artifacts and a quality assessment network to speed up the process of manual inspection.
This network achieved an overall AUC of 0.98. The quality assessment network was trained 400 annotated slides with binary labels. Additionally, we evaluated the qualitative performance of the network on an external test set originating from a different medical center. The segmentation network achieved an overall Dice score of 0.870. We trained the artifact segmentation network on 142 slides, coming from various tissue types and stainings. Using a threshold, technicians can choose how strict the quality assessment network filters out good and bad quality slides. It does this by generating a quality assessment score for digitized images, ranging between 0 and 1, which indicates the overall quality. Second, a quality assessment network that helps to speed up the process of manual inspection by technicians. AI algorithms can use the output segmentation mask to exclude artifact regions of an image during automated analysis. It does this by detecting and highlighting artifact regions in digitized images. First, an artifact segmentation network to make other AI algorithms more robust against artifacts. The two main goals of this AI system are to speed up the process of digitization of slides and to make other AI algorithms more robust against artifacts. Additionally, the system provides a quality score for technicians to speed up the inspection process. We propose a solution to this problem in the form of an AI system that detects and highlights regions containing artifacts in whole slide images using deep learning methods. The high volume of the slides and the sheer size of the scanned images (reaching up to 100,000 x 200,000 pixels) make manual control and supervision a highly time-consuming task for technicians. Approximately 1.7% of 950 scanned slides are rescanned daily due to having too poor quality.
In the Pathology department of Radboudumc, every scanned whole slide image is manually inspected on its quality by technicians. Here is an example image with artifacts causing an AI algorithm to make incorrect predictions Additionally, the quality system could provide recommendations, such as rescanning the whole slide image or leaving out defect regions of the slide when making diagnoses. A quality control mechanism is needed to ensure that whole slide images are of good enough quality to be further analyzed by pathologists. As a result, an AI system can fail to make a correct diagnosis in those regions.
The image below shows examples of common types of artifacts found in whole slide images (a) out-of-focus, (b) tissue folds, (c) ink, (d) dust, (e) pen mark, and (f) air bubbles.ĭepending on the severity of artifacts that are present, tissue regions that are important for diagnosis could be unclear, unusable, or even be completely missing. One of the most important challenges arises from the presence of image artifacts.
However, the digitization process also brought along new challenges for the automated analysis of digitized images. Whole slide imaging technology allowed the digitization of conventional glass slides, which led to several new opportunities in the Pathology field, such as the integration of computational systems, most notably artificial intelligence (AI).
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