A Study of Domain Generalization on Ultrasound-based Multi-Class Segmentation of Arteries, Veins, Ligaments, and Nerves Using Transfer Learning

• URL: http://arxiv.org/abs/2011.07019v1
• Date: Fri, 13 Nov 2020 16:59:20 GMT
• Title: A Study of Domain Generalization on Ultrasound-based Multi-Class Segmentation of Arteries, Veins, Ligaments, and Nerves Using Transfer Learning
• Authors: Edward Chen and Tejas Sudharshan Mathai and Vinit Sarode and Howie Choset and John Galeotti
• Abstract summary: We study the US-based segmentation of multiple classes through transfer learning by fine-tuning different contiguous blocks within the model. We propose a simple method for predicting generalization on unseen datasets and observe statistically significant differences between the fine-tuning methods.
• Score: 12.869637050331306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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