Search for temporal cell segmentation robustness in phase-contrast microscopy videos

Proceeding of: Medical Imaging with Deep Learning (MIDL 2022), Zürich, Switzerland, 6-8 July 2022This work presents a deep learning-based workflow to segment cancer cells embedded in D collagen matrices and imaged with phase-contrast microscopy under low magnification and strong background noise conditions. Due to the experimental and imaging setup, cell and protrusion appearance change largely from frame to frame. We use transfer learning and recurrent convolutional long-short term memory units to exploit the temporal information and provide temporally stable results. Our results show that the proposed approach is robust to weight initialization and training data sampling.This work was co-financed by ERDF, "A way of making Europe" (AMB), partially funded under Grant PID2019-109820RB-I00, MCIN/AEI/10.13039/501100011033/; the US NIH under Grants UO1AG060903 (DW) and U54CA143868 (DW). We acknowledge NVIDIA Corporation for the donation of the Titan X (Pascal) GPU