Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation

Jianning Li*, Antonio Pepe, Christina Gsaxner, Yuan Jin, Jan Egger

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

Medical images, especially volumetric images, are of high resolution and often exceed the capacity of standard desktop GPUs. As a result, most deep learning-based medical image analysis tasks require the input images to be downsampled, often substantially, before these can be fed to a neural network. However, downsampling can lead to a loss of image quality, which is undesirable especially in reconstruction tasks, where the fine geometric details need to be preserved. In this paper, we propose that high-resolution images can be reconstructed in a coarse-to-fine fashion, where a deep learning algorithm is only responsible for generating a coarse representation of the image, which consumes moderate GPU memory. For producing the high-resolution outcome, we propose two novel methods: learned voxel rearrangement of the coarse output and hierarchical image synthesis. Compared to the coarse output, the high-resolution counterpart allows for smooth surface triangulation, which can be 3D-printed in the highest possible quality. Experiments of this paper are carried out on the dataset of AutoImplant 2021 (https://autoimplant2021.grand-challenge.org/ ), a MICCAI challenge on cranial implant design. The dataset contains high-resolution skulls that can be viewed as 2D manifolds embedded in a 3D space. Codes associated with this study can be accessed at https://github.com/Jianningli/voxel_rearrangement.

Originalspracheenglisch
TitelTowards the Automatization of Cranial Implant Design in Cranioplasty II
UntertitelSecond Challenge, AutoImplant 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
Redakteure/-innenJianning Li, Jan Egger, Jianning Li, Jan Egger
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten45-62
Seitenumfang18
ISBN (Print)9783030926519
DOIs
PublikationsstatusVeröffentlicht - 2021
Veranstaltung2nd Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention: MICCAI 2021 - Virtuell, Österreich
Dauer: 1 Okt. 20211 Okt. 2021

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band13123 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz2nd Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention
KurztitelMICCAI 2021
Land/GebietÖsterreich
OrtVirtuell
Zeitraum1/10/211/10/21

ASJC Scopus subject areas

  • Theoretische Informatik
  • Allgemeine Computerwissenschaft

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