  Identifying landmarks in the femoral area is crucial for ultrasound (US)
-based robot-guided catheter insertion, and their presentation varies when
imaged with different scanners. As such, the performance of past deep
learning-based approaches is also narrowly limited to the training data
distribution; this can be circumvented by fine-tuning all or part of the model,
yet the effects of fine-tuning are seldom discussed. In this work, we study the
US-based segmentation of multiple classes through transfer learning by
fine-tuning different contiguous blocks within the model, and evaluating on a
gamut of US data from different scanners and settings. We propose a simple
method for predicting generalization on unseen datasets and observe
statistically significant differences between the fine-tuning methods while
working towards domain generalization.
