Transfer learning (TL) for medical image segmentation helps deep learning models achieve more accurate performances when there are scarce medical images. This study focuses on completing segmentation of the ribs from lung ultrasound images and finding the best TL technique with U-Net, a convolutional neural network for precise and fast image segmentation. Two approaches of TL were used, using a pre-trained VGG16 model to build the U-Net (V-Unet) and pre-training U-Net network with grayscale natural salient object dataset (X-Unet). Visual results and dice coefficients (DICE) of the models were compared. X-Unet showed more accurate and artifact-free visual performances on the actual mask prediction, despite its lower DICE than V-Unet. A partial-frozen network fine-tuning (FT) technique was also applied to X-Unet to compare results between different FT strategies, which FT all layers slightly outperformed freezing part of the network. The effect of dataset sizes was also evaluated, showing the importance of the combination between TL and data augmentation.

翻译：医疗图像分解的转移学习(TL)有助于在医疗图像稀少的情况下,深层次学习模型取得更准确的性能。这项研究的重点是完成肺超声图像肋骨的分解,并找到与U-Net(一个精确和快速图像分解的进化神经网络)相比的最佳TL技术。TL的两种方法是:使用预先训练的VGG16(V-Unet)模型来建立U-Net(V-Unet)和带有灰度自然显要物体数据集(X-Unet)的训练前U-Net网络。比较了模型的视觉结果和骰子系数(DICE)。X-Unet在实际遮罩预测中显示的更准确和无文物直观性表现,尽管其DICE比V-Unet低。对X-Unet(FT)也应用了部分冻结网络微调(FT)技术来比较不同FT战略(FT)之间的结果,后者的所有层次都略高于网络的冻结部分。还评估了数据集大小的影响,显示TL和数据放大之间组合的重要性。