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Faster dense deform...
Faster dense deformable image registration by utilizing both CPU and GPU
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- Ekström, Simon, 1991- (author)
- Uppsala universitet,Radiologi,Antaros Medical AB (Sweden)
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- Pilia, Martino (author)
- Uppsala universitet,Radiologi
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- Kullberg, Joel, 1979- (author)
- Uppsala universitet,Radiologi,Antaros Medical AB (Sweden)
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- Ahlström, Håkan, 1953- (author)
- Uppsala universitet,Radiologi,Antaros Medical AB (Sweden)
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- Strand, Robin, 1978- (author)
- Uppsala universitet,Radiologi,Institutionen för informationsteknologi
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- Malmberg, Filip, 1980- (author)
- Uppsala universitet,Radiologi
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(creator_code:org_t)
- 2021
- 2021
- English.
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In: Journal of Medical Imaging. - 2329-4302 .- 2329-4310. ; 8:1
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https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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Abstract
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- Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants. Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software. Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively. Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Keyword
- Atlas-based segmentation
- brain MRI
- deformable image registration
- graphics processing unit
Publication and Content Type
- ref (subject category)
- art (subject category)
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Ekström, Simon, ...
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Pilia, Martino
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Kullberg, Joel, ...
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Ahlström, Håkan, ...
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Strand, Robin, 1 ...
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Malmberg, Filip, ...
- About the subject
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- ENGINEERING AND TECHNOLOGY
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ENGINEERING AND ...
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and Medical Engineer ...
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and Medical Image Pr ...
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Journal of Medic ...
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Uppsala University