Training nuclei detection algorithms with simple annotations

• Main Authors: Henning Kost (Author), André Homeyer (Author), Jesper Molin (Author), Claes Lundström (Author), Horst Karl Hahn (Author)
• Format: Book
• Published: Elsevier, 2017-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2153-3539; 2153-3539; 10.4103/jpi.jpi_3_17
• Keywords: Active learning; machine learning; nuclei detection; training set generation; Computer applications to medicine. Medical informatics; R858-859.7; Pathology; RB1-214
• Source: Journal of Pathology Informatics, Vol 8, Iss 1, Pp 21-21 (2017)

Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. Results: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. Conclusions: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets.