In Domain Transfer Learning for Prostate MRI Segmentation

In recent years, deep learning has been widely applied to different medical image analysis tasks. However, large-scale annotated datasets are typically unavailable in such a domain, potentially hindering deep learning applications. Transfer learning with a fine-tuning framework is a commonly adopted solution to this issue, exploiting large-scale natural image datasets (e.g., ImageNet) to pre-train a deep neural network, and fine-tuning the resulting model on the target dataset. A potential alternative could be gathering data coming from different specialized centers to increase the number of available training data. However, privacy issues as well as diverse acquisition modalities are important challenges to such a solution. In this paper, we investigate if small-scale datasets for in-domain fine-tuning can be beneficial over natural image datasets pre-training only. Using popular small-scale benchmark datasets of prostate MRI volumes and ImageNet pre-trained models, we show that there is always a benefit when using in-domain data to fine-tune the ImageNet pretrained model, before fine-tuning it on the target dataset. Our results provide insights for a potential improvement of deeplearning-based prostate segmentation in MRI images, showing benefits when using data acquired in different specialized centers within a transfer learning framework.