Image segmentation is a branch of computer vision that is widely used in real world applications including biomedical image processing. With recent advancement of deep learning, image segmentation has achieved at a very high level performance. Recently, UNet architecture is found as the core of novel deep learning segmentation methods. In this paper we implement UNet architecture from scratch with using basic blocks in Pytorch and evaluate its performance on multiple biomedical image datasets. We also use transfer learning to apply novel modified UNet segmentation packages on the biomedical image datasets. We fine tune the pre-trained transferred model with each specific dataset. We compare its performance with our fundamental UNet implementation. We show that transferred learning model has better performance in image segmentation than UNet model that is implemented from scratch.

翻译：图像分割是计算机视觉的一个分支，广泛应用于包括生物医学图像处理在内的实际场景。随着深度学习的近期发展，图像分割已达到非常高的性能水平。近年来，UNet架构被认定为新型深度学习分割方法的核心。本文使用PyTorch中的基础模块从零实现UNet架构，并在多个生物医学图像数据集上评估其性能。同时，我们采用迁移学习将新型改进的UNet分割模型应用于生物医学图像数据集，通过微调针对特定数据集的预训练迁移模型，并将其性能与基础UNet实现进行对比。结果表明，迁移学习模型在图像分割任务中优于从零实现的UNet模型。