Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection.

ABSTRACT:

Background

Digital pathology enables remote access or consults and powerful image analysis algorithms. However, the slide digitization process can create artifacts such as out-of-focus (OOF). OOF is often only detected on careful review, potentially causing rescanning, and workflow delays. Although scan time operator screening for whole-slide OOF is feasible, manual screening for OOF affecting only parts of a slide is impractical.

Methods

We developed a convolutional neural network (ConvFocus) to exhaustively localize and quantify the severity of OOF regions on digitized slides. ConvFocus was developed using our refined semi-synthetic OOF data generation process and evaluated using seven slides spanning three different tissue and three different stain types, each of which we

SUBMITTER: Kohlberger T 

PROVIDER: S-EPMC6939343 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature