Rheumatoid arthritis (RA) is an inflammatory disease which afflicts the joints with arthritis and periarticular bone destruction as a result. One of its central features is bone erosion, a consequence of excessive bone resorption and insufficient bone formation. High-resolution peripheral quantitative computed tomography (HR-pQCT) is a promising tool for monitoring RA. Quantification of bone erosions and detection of possible progression is essential in the management of treatment. Detection is performed manually and is a very demanding task as rheumatologists must annotate hundreds of 2D images and inspect any region of the bone structure that is suspected to be a sign of RA. We propose a 2D based method which combines an accurate segmentation of bone surface boundary and classification of patches along the surface as healthy or eroded. We use a series of classical image processing methods to segment CT volumes semi-automatically. They are used as training data for a U-Net. We train a Siamese net to learn the difference between healthy and eroded patches. The Siamese net alleviates the problem of highly imbalanced class labels by providing a base for one-shot learning of differences between patches. We trained and tested the method using 3 full HR-pQCT scans with bone erosion of various size. The proposed pipeline succeeded in classifying healthy and eroded patches with high precision and recall. The proposed algorithm is a preliminary work to demonstrate the potential of our pipeline in automating the process of detecting and locating the eroded regions of bone surfaces affected by RA.