Automated Learning for Deformable Medical Image Registration by Jointly
Optimizing Network Architectures and Objective Functions
- URL: http://arxiv.org/abs/2203.06810v4
- Date: Sat, 12 Aug 2023 03:55:11 GMT
- Title: Automated Learning for Deformable Medical Image Registration by Jointly
Optimizing Network Architectures and Objective Functions
- Authors: Xin Fan, Zi Li, Ziyang Li, Xiaolin Wang, Risheng Liu, Zhongxuan Luo
and Hao Huang
- Abstract summary: This paper proposes an automated learning registration algorithm (AutoReg) that cooperatively optimize both architectures and their corresponding training objectives.
We conduct image registration experiments on multi-site volume datasets and various registration tasks.
Our results show that our AutoReg may automatically learn an optimal deep registration network for given volumes and achieve state-of-the-art performance.
- Score: 69.6849409155959
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deformable image registration plays a critical role in various tasks of
medical image analysis. A successful registration algorithm, either derived
from conventional energy optimization or deep networks requires tremendous
efforts from computer experts to well design registration energy or to
carefully tune network architectures for the specific type of medical data. To
tackle the aforementioned problems, this paper proposes an automated learning
registration algorithm (AutoReg) that cooperatively optimizes both
architectures and their corresponding training objectives, enabling
non-computer experts, e.g., medical/clinical users, to conveniently find
off-the-shelf registration algorithms for diverse scenarios. Specifically, we
establish a triple-level framework to deduce registration network architectures
and objectives with an auto-searching mechanism and cooperating optimization.
We conduct image registration experiments on multi-site volume datasets and
various registration tasks. Extensive results demonstrate that our AutoReg may
automatically learn an optimal deep registration network for given volumes and
achieve state-of-the-art performance, also significantly improving computation
efficiency than the mainstream UNet architectures (from 0.558 to 0.270 seconds
for a 3D image pair on the same configuration).
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