Morphological-consistent Diffusion Network for Ultrasound Coronal Image Enhancement Article Swipe
Yihao Zhou
,
Zixun Huang
,
Timothy Tin‐Yan Lee
,
Chonglin Wu
,
Kelly Ka‐Lee Lai
,
Deyou Yang
,
Alec Lik-Hang Hung
,
Jack C. Y. Cheng
,
Tsz Ping Lam
,
Yong‐Ping Zheng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.16661
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.16661
Ultrasound curve angle (UCA) measurement provides a radiation-free and reliable evaluation for scoliosis based on ultrasound imaging. However, degraded image quality, especially in difficult-to-image patients, can prevent clinical experts from making confident measurements, even leading to misdiagnosis. In this paper, we propose a multi-stage image enhancement framework that models high-quality image distribution via a diffusion-based model. Specifically, we integrate the underlying morphological information from images taken at different depths of the 3D volume to calibrate the reverse process toward high-quality and high-fidelity image generation. This is achieved through a fusion operation with a learnable tuner module that learns the multi-to-one mapping from multi-depth to high-quality images. Moreover, the separate learning of the high-quality image distribution and the spinal features guarantees the preservation of consistent spinal pose descriptions in the generated images, which is crucial in evaluating spinal deformities. Remarkably, our proposed enhancement algorithm significantly outperforms other enhancement-based methods on ultrasound images in terms of image quality. Ultimately, we conduct the intra-rater and inter-rater measurements of UCA and higher ICC (0.91 and 0.89 for thoracic and lumbar angles) on enhanced images, indicating our method facilitates the measurement of ultrasound curve angles and offers promising prospects for automated scoliosis diagnosis.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.16661
- https://arxiv.org/pdf/2409.16661
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403784306
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403784306Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.16661Digital Object Identifier
- Title
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Morphological-consistent Diffusion Network for Ultrasound Coronal Image EnhancementWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-09-25Full publication date if available
- Authors
-
Yihao Zhou, Zixun Huang, Timothy Tin‐Yan Lee, Chonglin Wu, Kelly Ka‐Lee Lai, Deyou Yang, Alec Lik-Hang Hung, Jack C. Y. Cheng, Tsz Ping Lam, Yong‐Ping ZhengList of authors in order
- Landing page
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https://arxiv.org/abs/2409.16661Publisher landing page
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https://arxiv.org/pdf/2409.16661Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2409.16661Direct OA link when available
- Concepts
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Coronal plane, Diffusion, Ultrasound, Image (mathematics), Radiology, Computer science, Medicine, Artificial intelligence, Physics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.evaluating | 135 |
| abstract_inverted_index.evaluation | 10 |
| abstract_inverted_index.guarantees | 119 |
| abstract_inverted_index.indicating | 180 |
| abstract_inverted_index.ultrasound | 15, 149, 187 |
| abstract_inverted_index.underlying | 60 |
| abstract_inverted_index.Remarkably, | 138 |
| abstract_inverted_index.Ultimately, | 156 |
| abstract_inverted_index.enhancement | 45, 141 |
| abstract_inverted_index.facilitates | 183 |
| abstract_inverted_index.generation. | 83 |
| abstract_inverted_index.information | 62 |
| abstract_inverted_index.inter-rater | 162 |
| abstract_inverted_index.intra-rater | 160 |
| abstract_inverted_index.measurement | 4, 185 |
| abstract_inverted_index.multi-depth | 102 |
| abstract_inverted_index.multi-stage | 43 |
| abstract_inverted_index.outperforms | 144 |
| abstract_inverted_index.deformities. | 137 |
| abstract_inverted_index.descriptions | 126 |
| abstract_inverted_index.distribution | 51, 114 |
| abstract_inverted_index.high-quality | 49, 79, 104, 112 |
| abstract_inverted_index.measurements | 163 |
| abstract_inverted_index.multi-to-one | 99 |
| abstract_inverted_index.preservation | 121 |
| abstract_inverted_index.Specifically, | 56 |
| abstract_inverted_index.high-fidelity | 81 |
| abstract_inverted_index.measurements, | 32 |
| abstract_inverted_index.misdiagnosis. | 36 |
| abstract_inverted_index.morphological | 61 |
| abstract_inverted_index.significantly | 143 |
| abstract_inverted_index.radiation-free | 7 |
| abstract_inverted_index.diffusion-based | 54 |
| abstract_inverted_index.enhancement-based | 146 |
| abstract_inverted_index.difficult-to-image | 23 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile |