A Two-Stage Localization and Refinement Neural Network Structure for Data-Efficient Microbleed Detection

Background/Objectives: In medical diagnostics, (semi-)automatic detection of pathological structures in images is becoming increasingly important. In particular, detecting cerebral microbleeds (CMBs) poses a challenge in clinical practice because the process is time-consuming and prone to error. Methods: Compared to previous methods of (semi-) automatic CMB detection that rely on large training datasets, we propose a method that can be adapted with a small dataset while still performing well. We propose a workflow that uses a two-stage approach to detect cerebral microbleeds that can be trained with a small dataset. The first stage is a 3D U-Net that retrieves potential CMB locations in the SWI image volume. Then, a 3D convolutional neural network (CNN) is used for discrimination to distinguish between real CMB and CMB mimics. Results: Using a dataset of 15 MRI scans with 40 marked CMBs, we are able to achieve a sensitivity of 97.5%. Conclusions: We showed that it is possible to create a workflow with high sensitivity using only a few training samples, enabling smaller radiological facilities to train networks using their own datasets. Even though the workflow performs well on a small dataset, it still requires further testing with other larger datasets.