Cell Selection-Based Data Reduction Pipeline for Whole Slide Image Analysis of Acute Myeloid Leukemia

Abstract

Computer-aided analyses of cells in Whole Slide Images (WSIs) have become an important topic in digital pathology. Despite the recent success of deep learning in biomedical research, these methods are still difficult to apply to multi-gigabyte WSIs. To overcome this difficulty, a variety of patch-based solutions have been introduced, which however all suffer from certain limitations compared to manual examinations and often fail to meet the specificities of cytological inspections. Here we introduce an alternative scheme which incorporates clinical expertise in the selection process to automatically identify the clinically relevant areas. By using a bone marrow smear dataset containing 22-gigapixel images of 153 patients, we introduce a novel pipeline combining unsupervised and supervised methodologies to gradually select the most appropriate single-cell regions, which are subsequently used in multiple medically crucial Acute Myeloid Leukemia (AML) predictions. Our approach is capable of dealing with a variety of common WSI challenges, massively limits the manual annotation effort, reduces the data by a factor of up to 99.9% and achieves super-human performance on the final cytological prediction tasks.