# Deep Learning for Semantic Segmentation of 3D Ultrasound Data Source: [Deep Learning for Semantic Segmentation of 3D Ultrasound Data]() Collection index: [llms.txt]() Data updated: 2026-01-22T05:00:31.014Z Authors: Chenyu Liu, Marco Cecotti, Harikrishnan Vijayakumar, Patrick Robinson, James Barson, Mihai Caleap Published: 2026-01-19T17:54:59.000Z ## Abstract Developing cost-efficient and reliable perception systems remains a central challenge for automated vehicles. LiDAR and camera-based systems dominate, yet they present trade-offs in cost, robustness and performance under adverse conditions. This work introduces a novel framework for learning-based 3D semantic segmentation using Calyo Pulse, a modular, solid-state 3D ultrasound sensor system for use in harsh and cluttered environments. A 3D U-Net architecture is introduced and trained on the spatial ultrasound data for volumetric segmentation. Results demonstrate robust segmentation performance from Calyo Pulse sensors, with potential for further improvement through larger datasets, refined ground truth, and weighted loss functions. Importantly, this study highlights 3D ultrasound sensing as a promising complementary modality for reliable autonomy. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on 3D Ultrasound Data left Pulse sensor baseline (test)](): Semantic segmentation; 3D Ultrasound Data left Pulse sensor baseline (test); Recall: 99.63. ## Original sources and implementation [Original paper]() [PDF]()