Annotation Efforts in Image Segmentation can be Reduced by Neural Network Bootstrapping

Abstract

Modern medical technology offers potential for the automatic generation of datasets that can be fed into deep learning systems. However, even though raw data for supporting diagnostics can be obtained with manageable effort, generating annotations is burdensome and time-consuming. Since annotating images for semantic segmentation is particularly exhausting, methods to reduce the human effort are especially valuable. We propose a combined framework that utilizes unsupervised machine learning to automatically generate segmentation masks. Experiments on two biomedical datasets show that our approach generates noticeably better annotations than Otsu thresholding and k-means clustering without needing any additional manual effort. Using our framework, unannotated datasets can be amended with pre-annotations fully unsupervised thus reducing the human effort to a minimum.

Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation

“…Such methods are gradually recognized as a limiting factor [37]. Further, human annotations are often erroneous, so numerous works try to reduce the human component in ML or focus on data-efficient learning approaches like active learning, semisupervised learning, or transfer learning [44]- [46], [50], [51], [58], [69], [70]. A particularly promising approach to reduce annotation efforts is SSL, which finds inherent structures in unannotated images [33].…”