Abstract
With recent advances in multiplexed imaging and spatial transcriptomic and proteomic technologies, cell segmentation is becoming a crucial step in biomedical image analysis. In recent years, Fully Convolutional Networks (FCN) have achieved great success in nuclei segmentation in in vitro imaging. Nevertheless, it remains challenging to perform similar tasks on in situ tissue images with more cluttered cells of diverse shapes. To address this issue, we propose a novel transfer learning, cell segmentation framework incorporating shape-aware features in a deep learning model, with multi-level watershed and morphological post-processing steps. Our results show that incorporation of geometric features improves generalizability to segmenting cells in in situ tissue images, using solely in vitro images as training data.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
yj2589{at}columbia.edu, aat2167{at}columbia.edu
LMRL Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020)