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UltrON: Ultrasound Occupancy Networks

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Link to Profile Nassir Navab PI Matchmaking

Nassir Navab

Prof. Dr.

Principal Investigator

Computer Aided Medical Procedures & Augmented Reality

Abstract

In free-hand ultrasound imaging, sonographers rely on expertise to mentally integrate partial 2D views into 3D anatomical shapes. Shape reconstruction can assist clinicians in this process. Central to this task is the choice of shape representation, as it determines how accurately and efficiently the structure can be visualized, analyzed, and interpreted. Implicit representations, such as SDF and occupancy function, offer a powerful alternative to traditional voxel- or mesh-based methods by modeling continuous, smooth surfaces with compact storage, avoiding explicit discretization. Recent studies demonstrate that SDF can be effectively optimized using annotations derived from segmented B-mode ultrasound images. Yet, these approaches hinge on precise annotations, overlooking the rich acoustic information embedded in B-mode intensity. Moreover, implicit representation approaches struggle with the ultrasound's view-dependent nature and acoustic shadowing artifacts, which impair reconstruction. To address the problems resulting from occlusions and annotation dependency, we propose an occupancy-based representation and introduce gls{UltrON} that leverages acoustic features to improve geometric consistency in weakly-supervised optimization regime. We show that these features can be obtained from B-mode images without additional annotation cost. Moreover, we propose a novel loss function that compensates for view-dependency in the B-mode images and facilitates occupancy optimization from multiview ultrasound. By incorporating acoustic properties, gls{UltrON} generalizes to shapes of the same anatomy. We show that gls{UltrON} mitigates the limitations of occlusions and sparse labeling and paves the way for more accurate 3D reconstruction.

inproceedings

MICCAI 2025

28th International Conference on Medical Image Computing and Computer Assisted Intervention. Daejeon, Republic of Korea, Sep 23-27, 2025.
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A Conference

Authors

M. Wysocki • F. Dülmer • A. Bal • N. Navab • M. F. Azampour

Links

DOI GitHub

Research Area

 C1 | Medicine

BibTeXKey: WDB+25

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