High Resolution U-Net for Quantitatively Analyzing Early Spatial Patterning of Human Induced Pluripotent Stem Cells on Micropatterns. - Slo-Li Chu; Kuniya Abe; Hideo Yokota; Dooseon Cho; Yuan-Hao Chen; Ming-Dar Tsai (EMBC, 2021)

Visualization and quantitative analyses for mouse embryonic stem cell tracking by manipulating hierarchical data structures using time-lapse confocal microscopy images. - Hideo Yokota; Kuniya Abe; Yuan-Hsiang Chang; Dooseon Cho; Ming-Dar Tsai; Pin-Han Huang (EMBC, 2021)

U-net structures for segmentation of single mouse embryonic stem cells using three-dimensional confocal microscopy images. - Slo-Li Chu; Hideo Yokota; Kuniya Abe; Dooseon Cho; Ming-Dar Tsai (EMBC, 2022)

Quantitative Analyses for Early Tempo-spatial Patterning of Differentiated Human Induced Pluripotent Stem Cells on Micropatterns using Time-lapse Bright-field Microscopy Images. - Slo-Li Chu; Kuniya Abe; Kuan-Ting Lin; Hideo Yokota; Dooseon Cho; Ming-Dar Tsai (EMBC, 2023)

Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells by Deep Learning for Cell Center Detection using Three-Dimensional Confocal Fluorescence Microscopy Images. - Slo-Li Chu; Hideo Yokota; Hao-Lun Hsieh; Kuniya Abe; Dooseon Cho; Ming-Dar Tsai (EMBC, 2023)

Deep Learning Method for Estimating Germ-layer Regions of Early Differentiated Human Induced Pluripotent Stem Cells on Micropattern Using Bright-field Microscopy Image. - Slo-Li Chu; Hideo Yokota; Pai-Ting Wang; Kuniya Abe; Yohei Hayashi; Dooseon Cho; Ming-Dar Tsai (EMBC, 2024)