Hao Song
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Hao Song has a total of 19 co-authors. Here are the top co-authors by number of shared publications:
- Yongxia Yang
National Tobacco Cultivation & Physiology & Biochemistry Research Centre, College of Tobacco Science, Henan Agricultural University, Zhengzhou, 450002, China
2 shared publications - Hanh Thi Thuy Nguyen
Faculty of Biotechnology, Vietnam National University of Agriculture, Hanoi 12406, Vietnam
1 shared publication - Hongfang Jia
College of Tobacco Science, Henan Agricultural University, Zhengzhou, China
1 shared publication - Huajin Sheng
Global Institute for Food Security, University of Saskatchewan, Saskatoon, Canada
1 shared publication - Jurandir Vieira Magalhaes
Embrapa Maize and Sorghum, Brazil
1 shared publication - Karim Panjvani
Global Institute for Food Security, University of Saskatchewan, Saskatoon, Canada
1 shared publication
Latest Publications
- Open Access
- 30 July 2026
- Published by Springer Nature in npj Imaging
- Vol. 4 (1) , 53
Review of image segmentation techniques for biomedical micro-CT: from laboratory absorption imaging to synchrotron phase-contrast
X-ray micro-computed tomography (μCT) is widely used in biomedical research for non-destructive, high-resolution imaging. Synchrotron Radiation Phase-Contrast μCT (SR-PCI-μCT) further enhances image quality with higher Signal to Noise Ratio (SNR), improved contrast, and faster acquisition. However, segmenting SR-PCI-μCT images remains challenging due to their heterogeneous image property and limited training data. Additionally, the increasing throughput of synchrotron facilities demands robust, efficient segmentation methods. This paper reviews a list of recent segmentation approaches in biomedical μCT. Traditional methods remain simple and effective for simple, high-contrast structures but require extensive tuning and generalize poorly to complex, low-contrast soft tissues. Data-driven models provide higher accuracy and robustness yet rely heavily on large expert-annotated datasets, limiting reproducibility and cross-dataset adaptability. Recent advance on vision transformers have shifted the paradigm from task-specified to more domain-specified segmentation, though these techniques are still evolving and require adaptation for SR-PCI-μCT studies. This survey provides the first bi-modality review covering both laboratory and synchrotron biomedical μCT segmentation. It consolidates recent segmentation methodologies, identifies major trends in deep-learning techniques, and highlights current limitations across SR-PCI-μCT. Additionally, it outlines open challenges to guide future research and practical advancements in biomedical μCT segmentation.
- 30 July 2026
- Published by Springer Nature in npj Imaging
- Vol. 4 (1) , 53
Open AccessReview of image segmentation techniques for biomedical micro-CT: from laboratory absorption imaging to synchrotron phase-contrast
X-ray micro-computed tomography (μCT) is widely used in biomedical research for non-destructive, high-resolution imaging. Synchrotron Radiation Phase-Contrast μCT (SR-PCI-μCT) further enhances image quality with higher Signal to Noise Ratio (SNR), improved contrast, and faster acquisition. However, segmenting SR-PCI-μCT images remains challenging due to their heterogeneous image property and limited training data. Additionally, the increasing throughput of synchrotron facilities demands robust, efficient segmentation methods. This paper reviews a list of recent segmentation approaches in biomedical μCT. Traditional methods remain simple and effective for simple, high-contrast structures but require extensive tuning and generalize poorly to complex, low-contrast soft tissues. Data-driven models provide higher accuracy and robustness yet rely heavily on large expert-annotated datasets, limiting reproducibility and cross-dataset adaptability. Recent advance on vision transformers have shifted the paradigm from task-specified to more domain-specified segmentation, though these techniques are still evolving and require adaptation for SR-PCI-μCT studies. This survey provides the first bi-modality review covering both laboratory and synchrotron biomedical μCT segmentation. It consolidates recent segmentation methodologies, identifies major trends in deep-learning techniques, and highlights current limitations across SR-PCI-μCT. Additionally, it outlines open challenges to guide future research and practical advancements in biomedical μCT segmentation.