December, 2019

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...

Diffusion MRI microstructural models in the cervical spinal cord – application, normative values, and correlations with histological analysis

Dec. 16, 2019—Kurt G. Schilling, Samantha By, Haley Feiler, Bailey Box, Kristin P. O’Grady, Atlee Witt, Bennett A. Landman, Seth A. Smith. “Diffusion MRI microstructural models in the cervical spinal cord – application, normative values, and correlations with histological analysis”. NeuroImage. doi: 10.1016/j.neuroimage.2019.116026. 2019. Full text: https://www.ncbi.nlm.nih.gov/pubmed/31326569 Abstract Multi-compartment tissue modeling using diffusion magnetic resonance imaging has proven...

Fig. 2. Distortion and correction in diffusion MRI. (A) EPI susceptibility distortion occurs along the phase encode direction, with phase encoding in the posterior (PE-P) direction leading to displacements of identical distance but opposite direction of that in the anterior (PE-A) direction. (B) State of the art distortion correction (topup) typically uses distortions in two opposite directions to iteratively estimate the undistorted image. (C) The proposed method uses an undistorted T1-weighted image to synthesize an undistorted volume with b0 contrast, which can be used to correct the distorted (in this case, PE-P) image without requiring an additional phase encoding acquisition. The blue circles highlight areas of observable signal distortion. Yellow arrows indicate phase encode direction. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Synthesized b0 for diffusion distortion correction (Synb0-DisCo).

Dec. 16, 2019—Kurt G Schilling, Yuankai Huo, Allen Newton, Colin Hansen, Vishwesh Nath, Andrea T. Shafer, Owen Williams, Susan M. Resnick, Baxter Rogers, Adam W Anderson, Bennett A Landman. “Synthesized b0 for diffusion distortion correction (Synb0-DisCo).” Magnetic Resonance Imaging. 2019 May 7. pii: S0730-725X(18)30617-9. doi: 10.1016/j.mri.2019.05.008 Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Synthesized+b0+for+diffusion+distortion+correction+(Synb0-DisCo). Abstract Diffusion magnetic resonance images typically suffer from spatial...

MRI correlates of chronic symptoms in mild traumatic brain injury

Dec. 6, 2019—Kerley, C. I., Schilling, K. G., Blaber, J., Miller, B., Newton, A., Anderson, A. W., Landman, B. A., Rex, T. S. “MRI correlates of chronic symptoms in mild traumatic brain injury.” In SPIE Medical Imaging, International Society for Optics and Photonics, 2020. Full text: NIHMSID, arXiv Abstract Some veterans with a history of mild traumatic brain injury (mTBI) have reported experiencing...

Qualitative results of the DN segmentation. Here, we show the output of the automatic segmentation using unimodal U-Net, a multisequence U-Net, and single atlas registration of the SUIT template in the same subject. The manual label is shown in green while the predicted label is shown in red. Overlap between the two structures is shown in yellow. The Dice coefficient is 0.95 for the unimodal T1 model, 0.95 for the unimodal T2 model, 0.97 for the unimodal FA model, 0.93 for the multisequence model, 0.41 for the SUIT atlas, and 0.43 for the multi-atlas segmentation. Note: the multiatlas segmentation results were registered to the SUIT template for visualization.

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...

Example of Internal-transfer Weighting (ITW) of multi-task learning. The LC, AA, CB, COPD, E represent the tasks of lung cancer, adult asthma, chronic bronchitis, chronic obstructive pulmonary disease and emphysema, respectively. The red number is the loss weight for the task. Note that we apply PFLP on top of the displayed weights.

Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection

Dec. 5, 2019—Yiyuan Yang, Riqiang Gao*, Yucheng Tang, Sanja L. Antic, Steve Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman, “Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection,” SPIE MI:IP 2020. Houston, TX. [full text] Abstract Deep learning has achieved many successes in medical imaging, including lung nodule segmentation and lung cancer prediction on...

Vision for joint consideration of diagnosis and cancer-free progression time (CFPT) in cancer and non-cancer patients. The blue dashed lines represent the defined CFPT. The red number (%) in small rectangle is the risk score computed by the PLCOm2012 [3] model. Current scores suggest screening at every point since they indicate high risk (>= 2%). If CFPT were known, the green rectangles could have been avoided.

Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging

Dec. 5, 2019—Riqiang Gao, Lingfeng Li, Yucheng Tang, Sanja L. Antic, Alexis B. Paulson, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman, “Deep Multi- task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging,” SPIE MI:IP 2020. Houston, TX. [full text] Abstract Annual low dose computed tomography (CT) lung screening is currently advised for individuals...