Learning Shape Reconstruction from Sparse Measurements with Neural Implicit Functions

Tamaz Amiranashvili, David Lüdke, Hongwei Li, bjoern menze, Stefan Zachow

Keywords: shape reconstruction, shape priors, neural implicit shape representations

Abstract: Reconstructing anatomical shapes from sparse or partial measurements relies on prior knowledge of shape variations that occur within a given population. Such shape priors are learned from example shapes, obtained by segmenting volumetric medical images. For existing models, the resolution of a learned shape prior is limited to the resolution of the training data. However, in clinical practice, volumetric images are often acquired with highly anisotropic voxel sizes, e.g. to reduce image acquisition time in MRI or radiation exposure in CT imaging. The missing shape information between the slices prohibits existing methods to learn a high-resolution shape prior. We introduce a method for high-resolution shape reconstruction from sparse measurements without relying on high-resolution ground truth for training. Our method is based on neural implicit shape representations and learns a continuous shape prior only from highly anisotropic segmentations. Furthermore, it is able to learn from shapes with a varying field of view and can reconstruct from various sparse input configurations. We demonstrate its effectiveness on two anatomical structures: vertebra and distal femur, and successfully reconstruct high-resolution shapes from sparse segmentations, using as few as three orthogonal slices.

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Paper Type: methodological development

Primary Subject Area: Segmentation

Secondary Subject Area: Learning with Noisy Labels and Limited Data

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TL;DR: We build a high-resolution shape prior from sparse, anisotropic data and apply it to various shape reconstruction tasks.

Code And Data:

Code:
https://github.com/menzelab/implicit-shape-reconstruction

Lumbar vertebra dataset (Verse19):
https://github.com/anjany/verse

Distal Femur dataset (OAI-ZIB):
https://pubdata.zib.de