Information
- Publication Type: Master Thesis
- Workgroup(s)/Project(s): not specified
- Date: 2026
- TU Wien Library: AC17859583
- Open Access: yes
- First Supervisor: Renata Raidou
- Pages: 122
- Keywords: Medical Image Analysis, Segmentation, Data Sampling, 3D Data Processing, Deep Learning, Image Reconstruction
Abstract
Efficient analysis and processing of 3D volumes are crucial in clinical practice. A common task in 3D medical image processing is object segmentation, where objects of interest are delineated. However, segmenting large volumetric scans requires substantial memory and computational power, making end-to-end segmentation both memory- and computationally intensive. A potential solution to reduce these costs is to select only a subset of the input, segment this subset, and estimate the values of the remaining regions by reconstructing them from the segmented subset. Our hypothesis is that the choice of subset influences reconstruction performance, with some regions of the input volume being more informative for reconstruction than others. To test this hypothesis, we propose a neural network capable of identifying subsets that contribute most to accurate reconstruction. To simplify the process and focus on the core task, we assume that binary segmentations of the objects of interest are provided and select subsets directly from them, reconstructing the original binary segmentations afterwards. We build our neural network upon an existing point cloud-based network that learns to select representative points, and integrate it into a novel end-to-end pipeline for reconstructing full volumes from limited input data. We modify the original point cloud–based loss function to operate on voxel grid data and introduce conversion and extraction mechanisms that enable transitions between voxel grid and point cloud representations. Our proposed pipeline first converts the input voxel grid into a point cloud representation to enable efficient geometric processing. A neural network architecture then processes the point cloud and predicts a set of candidate centers for volumetric patches. These predicted centers are subsequently used to extract the output patch set, which is then fed to the downstream reconstruction network. We evaluate our pipeline on two datasets of medical shape segmentations with varying geometrical complexity. Our experiments show that the proposed learned sampler identifies informative regions, which support reconstruction performance, especially for complex shapes and limited spatial context. We further evaluate the effect of reconstruction network quality across different input configurations, varying patch size and number of patches, and show that our approach is effective when reconstruction accuracy is poor or when the input shape has complex geometry. Finally, we analyze the computations and memory demands of the proposed pipeline, showing that the additional overhead remains under 1 GB of memory and 0.5 s of extra computation, making the method suitable for deployment in resource-limited environments.
Additional Files and Images
Weblinks
BibTeX
@mastersthesis{peterka-2026-ips,
title = "Informed Patch Sampling for 3D Medical Image Reconstruction",
author = "Ond\v{r}ej Peterka",
year = "2026",
abstract = "Efficient analysis and processing of 3D volumes are crucial
in clinical practice. A common task in 3D medical image
processing is object segmentation, where objects of interest
are delineated. However, segmenting large volumetric scans
requires substantial memory and computational power, making
end-to-end segmentation both memory- and computationally
intensive. A potential solution to reduce these costs is to
select only a subset of the input, segment this subset, and
estimate the values of the remaining regions by
reconstructing them from the segmented subset. Our
hypothesis is that the choice of subset influences
reconstruction performance, with some regions of the input
volume being more informative for reconstruction than
others. To test this hypothesis, we propose a neural network
capable of identifying subsets that contribute most to
accurate reconstruction. To simplify the process and focus
on the core task, we assume that binary segmentations of the
objects of interest are provided and select subsets directly
from them, reconstructing the original binary segmentations
afterwards. We build our neural network upon an existing
point cloud-based network that learns to select
representative points, and integrate it into a novel
end-to-end pipeline for reconstructing full volumes from
limited input data. We modify the original point
cloud–based loss function to operate on voxel grid data
and introduce conversion and extraction mechanisms that
enable transitions between voxel grid and point cloud
representations. Our proposed pipeline first converts the
input voxel grid into a point cloud representation to enable
efficient geometric processing. A neural network
architecture then processes the point cloud and predicts a
set of candidate centers for volumetric patches. These
predicted centers are subsequently used to extract the
output patch set, which is then fed to the downstream
reconstruction network. We evaluate our pipeline on two
datasets of medical shape segmentations with varying
geometrical complexity. Our experiments show that the
proposed learned sampler identifies informative regions,
which support reconstruction performance, especially for
complex shapes and limited spatial context. We further
evaluate the effect of reconstruction network quality across
different input configurations, varying patch size and
number of patches, and show that our approach is effective
when reconstruction accuracy is poor or when the input shape
has complex geometry. Finally, we analyze the computations
and memory demands of the proposed pipeline, showing that
the additional overhead remains under 1 GB of memory and 0.5
s of extra computation, making the method suitable for
deployment in resource-limited environments.",
pages = "122",
address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
school = "Research Unit of Computer Graphics, Institute of Visual
Computing and Human-Centered Technology, Faculty of
Informatics, TU Wien",
keywords = "Medical Image Analysis, Segmentation, Data Sampling, 3D Data
Processing, Deep Learning, Image Reconstruction",
URL = "https://www.cg.tuwien.ac.at/research/publications/2026/peterka-2026-ips/",
}
thesis