TY - GEN
T1 - Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation
AU - Li, Jianning
AU - Pepe, Antonio
AU - Gsaxner, Christina
AU - Jin, Yuan
AU - Egger, Jan
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - 3D printing
KW - Cranial implant design
KW - Deep learning
KW - Hamming distance
KW - Hash table
KW - Manifold
KW - Nearest neighbor search (NNS)
KW - Skull reconstruction
KW - Sparse CNN
KW - Super resolution
UR - http://www.scopus.com/inward/record.url?scp=85121914577&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-92652-6_5
DO - 10.1007/978-3-030-92652-6_5
M3 - Conference paper
AN - SCOPUS:85121914577
SN - 9783030926519
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 45
EP - 62
BT - Towards the Automatization of Cranial Implant Design in Cranioplasty II
A2 - Li, Jianning
A2 - Egger, Jan
A2 - Li, Jianning
A2 - Egger, Jan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd 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
Y2 - 1 October 2021 through 1 October 2021
ER -