PT  - JOURNAL ARTICLE
AU  - Jin, Yinuo
AU  - Toberoff, Alexandre
AU  - Azizi, Elham
TI  - Transfer learning framework for cell segmentation with incorporation of geometric features
AID  - 10.1101/2021.02.28.433289
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.02.28.433289
4099  - http://biorxiv.org/content/early/2021/03/10/2021.02.28.433289.short
4100  - http://biorxiv.org/content/early/2021/03/10/2021.02.28.433289.full
AB  - 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 StatementThe authors have declared no competing interest.