Few-Shot Microscopy Image Cell Segmentation

• Published in: Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track Vol. 12461; pp. 139 - 154
• Main Authors: Dawoud, Youssef, Hornauer, Julia, Carneiro, Gustavo, Belagiannis, Vasileios
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Cell segmentation; Few-shot learning; Microscopy image
• ISBN: 9783030676698; 3030676692
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-67670-4_9

Automatic cell segmentation in microscopy images works well with the support of deep neural networks trained with full supervision. Collecting and annotating images, though, is not a sustainable solution for every new microscopy database and cell type. Instead, we assume that we can access a plethora of annotated image data sets from different domains (sources) and a limited number of annotated image data sets from the domain of interest (target), where each domain denotes not only different image appearance but also a different type of cell segmentation problem. We pose this problem as meta-learning where the goal is to learn a generic and adaptable few-shot learning model from the available source domain data sets and cell segmentation tasks. The model can be afterwards fine-tuned on the few annotated images of the target domain that contains different image appearance and different cell type. In our meta-learning training, we propose the combination of three objective functions to segment the cells, move the segmentation results away from the classification boundary using cross-domain tasks, and learn an invariant representation between tasks of the source domains. Our experiments on five public databases show promising results from 1- to 10-shot meta-learning using standard segmentation neural network architectures.