Telecentric wide-field reflected light microscopic dataset

Multi-class segmentation of unlabelled living cells in time-lapse light
microscopy images is challenging due to the temporal behaviour and changes
in cell life cycles and the complexity of images of this kind. The
deep-learning-based methods achieved promising outcomes and remarkable
success in single- and multi-class medical and microscopy image
segmentation. The main objective of this study is to develop a hybrid
deep-learning-based categorical segmentation and classification method for
living HeLa cells in reflected light microscopy images. A symmetric simple
U-Net and three asymmetric hybrid convolution neural
networks---VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net were proposed
and mutually compared to find the most suitable architecture for
multi-class segmentation of our datasets.  The inception module
in the Inception-U-Net contained kernels with different sizes within the
same layer to extract all feature descriptors. The series of residual
blocks with the skip connections in each ResNet34-U-Net's level
alleviated the gradient vanishing problem and improved the generalisation
ability. The m-IoU scores of multi-class segmentation for our datasets
reached 0.7062, 0.7178, 0.7907, and 0.8067 for the simple U-Net,
VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net, respectively. For each
class and the mean value across all classes, the most accurate multi-class
semantic segmentation was achieved using the ResNet34-U-Net architecture
(evaluated as the m-IoU and Dice metrics).