Skip to main content

Image Segmentation Category

Distributed Deep Learning Across Multisite Datasets for Generalized CT Hemorrhage Segmentation

Jan. 2, 2020—Remedios, S. W., Roy, S., Bermudez, C., Patel, M. B., Butman, J. A., Landman, B. A., & Pham, D. L. (2019). Distributed Deep Learning Across Multi‐site Datasets for Generalized CT Hemorrhage Segmentation. Medical physics. Full Text: Pubmed Link Abstract Purpose: As deep neural networks achieve more success in the wide field of computer vision, greater emphasis is...

Read more


Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury

Jan. 2, 2020—Remedios, Samuel, et al. “Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury.” Medical Imaging 2019: Image Processing. Vol. 10949. International Society for Optics and Photonics, 2019. Full text: PubMed Link Abstract Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need...

Read more


Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision

Dec. 19, 2019—Ho Hin Lee, Yucheng Tang, Olivia Tang, Yuchen Xu, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman, “Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision”, SPIE MI:IP 2020. Houston, TX. Link: https://arxiv.org/abs/1911.05113 Abstract Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that...

Read more


Using deep learning for a diffusion-based segmentation of the dentate nucleus and its benefits over atlas-based methods

Dec. 6, 2019—Noguera, C. B., Bao, S., Petersen, K. J., Lopez, A. M., Reid, J., Plassard, A. J., … & Landman, B. A. (2019). Using deep learning for a diffusion-based segmentation of the dentate nucleus and its benefits over atlas-based methods. Journal of Medical Imaging, 6(4), 044007. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/31824980 Abstract The dentate nucleus (DN) is a...

Read more


Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning

Aug. 13, 2019—Bermudez, C., Blaber, J., Remedios, S.W., Reynolds, J.E., Lebel, C., McHugo, M., Heckers, S., Huo, Y., Landman, B.A. Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Constrast MRI with Augmented Transfer Learning. SPIE Medical Imaging: Image Processing 2020. Houston, TX. Full Text: NIHMSID Abstract Generalizability is an important problem in deep neural networks, especially in...

Read more


4D Multi-atlas Label Fusion using Longitudinal Images

Aug. 29, 2017—Yuankai Huo, Susan M. Resnick and Bennett A. Landman. “4D Multi-atlas Label Fusion using Longitudinal Images”. MICCAI Patch-MI Workshop, 2017. Full text: https://drive.google.com/open?id=0Bzzeqiij2Zara1ZlQXJiclM2UEE Abstract Longitudinal reproducibility is an essential concern in automated medical image segmentation, yet has proven to be an elusive objective as manual brain structure tracings have shown more than 10% variability. To improve reproducibility, longitudinal...

Read more


Gray Matter Surface based Spatial Statistics (GS-BSS) in Diffusion Microstructure

Aug. 28, 2017—Citation: Gray Matter Surface based Spatial Statistics (GS-BSS) in Diffusion Microstructure. Authors: Prasanna Parvatheni, Baxter P. Rogers, Yuankai Huo, Kurt G. Schilling, Allison E. Hainline, Adam W. Anderson, Neil D. Woodward, Bennett A. Landman. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer. (2017). Accepted.  Abstract Tract-based spatial statistics (TBSS) has proven to be...

Read more


Gray Matter Surface based Spatial Statistics in Neuroimaging Studies

Jun. 1, 2017—Citation: Gray Matter Surface based Spatial Statistics in Neuroimaging Studies. Authors: Prasanna Parvatheni, Baxter P. Rogers, Yuankai Huo, Kurt G. Schilling, Allison E. Hainline, Adam W. Anderson, Neil D. Woodward, Bennett A. Landman. Frontiers in Biomedical Imaging Science VI. May 2017. Abstract. Abstract In this study, we propose gray matter surface based spatial statistics (GS-BSS)...

Read more


Accurate Age Estimation in a Pediatric Population Using Deep Learning on T1‐weighted MRI Structural Features

May. 15, 2017—Citation: Bermudez, C. et.al. Accurate Age Estimation in a Pediatric Population Using Deep Learning on T1‐weighted MRI  Structural Features. Frontiers in Biomedical Imaging Science VI. May 2017. Abstract. Abstrract It is well known that there are structural changes that occur in the brain with age. However, there are insufficient imaging biomarkers that reliably describe structural...

Read more


Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion

Oct. 31, 2016—Yuankai Huo, Andrew J. Asman, Andrew J. Plassard, Bennett A. Landman. “Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion.” Human Brain Mapping. In Press October 2016 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27726243 Abstract Total intracranial volume (TICV) is an essential covariate in brain volumetric analyses. The prevalent brain imaging software packages provide automatic TICV estimates. FreeSurfer and FSL estimate TICV using a scaling factor while SPM12 accumulates probabilities of...

Read more