Medical Image Analysis

Jason Dowling
Team Leader
Medical Image Analysis

Our team partners with clinicians to develop and validate novel technology to, extract quantitative information from medical image data and to understand and improve disease diagnosis, treatment planning and treatment delivery.

In collaboration with clinical partners our team produce high impact scientific research in a range of disciplines. The team currently receive additional funding support from six NHMRC project grants, an NHMRC development grant, and two Advance Queensland Post-Doctoral Research Fellowships.

Impact on the Health System
Our projects develop cutting edge validated methods to acquire and automatically extract information from medical images to improve diagnosis, treatment delivery efficiency, cost-effectiveness and patient quality of life.

Our Solutions
Our solutions have been developed in partnership with healthcare practitioners at many sites across Australia.

 

Case Studies

 

Team Members:

Jason Dowling

Kerstin Pannek

Alex Pagnozzi

Lee Reid

Susmita Saha

Ashley Gillman

Miles Seidel

 

 

 

 

 

 

 

 

 

 

 

 

Jason Dowling
Team Leader, passionate about extracting information from medical images to improve the understanding, diagnosis and treatment of disease.
CSIRO Profile

Kerstin Pannek
Research Scientist, developing methods for prediction of outcomes for preterm-born infants

Alex Pagnozzi
Postdoctoral Fellow, developing image processing algorithms using structural MRI that provide important biomarkers to assist clinicians in the diagnosis and treatment of several childhood neurological conditions, including Cerebral Palsy.
CSIRO Profile

Lee Reid
Postdoctoral Fellow, developing a fully automated neurosurgical planning platform which incorporates information from structural MRI, advanced diffusion MRI, and advanced functional MRI..
CSIRO Profile

Susmita Saha
Postdoctoral Fellow, working on the application of deep learning/machine learning techniques on neonatal MRI for a very early prediction of Cerebral Palsy and later neurodevelopmental outcome.

Ashley Gillman
Postgraduate Student, working in motion correction of PET in combined PET/MR.

Miles Seidel
Postgraduate Student, working on image analysis of neonatal brain MRI for prediction of neurodevelopmental outcomes.

Publications

  1. Chandra, SS., Dowling, JA., Engstrom, C., Xia, Y., Paproki, A., Neubert, A., Rivest-Hénault, D., Salvado, O., Crozier, S., Fripp, J. (2018). A lightweight rapid application development framework for biomedical image analysis. Computer Methods and Programs in Biomedicine, 164, 193-205. https://doi.org/10.1016/j.cmpb.2018.07.011
  2. Pannek, K., Fripp, J., George, J. M., Fiori, S., Colditz, P. B., Boyd, R. N., & Rose, S. E. (2018). Fixel-based analysis reveals alterations is brain microstructure and macrostructure of preterm-born infants at term equivalent age. NeuroImage: Clinical, 18, 51–59. https://doi.org/10.1016/j.nicl.2018.01.003
  3. Pagnozzi AM, Dowson N, Doecke J, Fiori S, Bradley AP, Boyd RN, Rose S (2016): Automated, quantitative measures of grey and white matter lesion burden correlates with motor and cognitive function in children with unilateral cerebral palsy. NeuroImage Clin 11:751–759. https://doi.org/10.1016/j.nicl.2016.05.018
  4. Pagnozzi AM, Dowson N, Fiori S, Doecke J, Bradley AP, Boyd RN, Rose S (2016): Alterations in regional shape on ipsilateral and contralateral cortex contrast in children with unilateral cerebral palsy and are predictive of multiple outcomes. Hum Brain Mapp. 37:3588-3603. https://doi.org/10.1002/hbm.23262
  5. Pagnozzi AM, Shen KK, Doecke J, Boyd RN, Bradley AP, Rose S, Dowson N (2016): Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy. Hum Brain Mapp. 37:3795-3809. https://doi.org/10.1002/hbm.23276
  6. George, J. M., Fiori, S., Fripp, J., Pannek, K., Bursle, J., Moldrich, R. X., … Boyd, R. N. N. (2017). Validation of an MRI Brain Injury and Growth Scoring System in Very Preterm Infants Scanned at 29- to 35-Week Postmenstrual Age. American Journal of Neuroradiology, 38(7), 1435–1442. https://doi.org/10.3174/ajnr.A5191
  7. Pagnozzi AM, Gal Y, Boyd RN, Fiori S, Fripp J, Rose S, Dowson N (2015) The need for improved brain lesion segmentation techniques for children with cerebral palsy: a review. Int. J. of Develop. Neurosci. 47:229-246. https://doi.org/10.1016/j.ijdevneu.2015.08.004
  8. Rivest-Hénault D, Dowson N, Greer PB, Fripp J, Dowling JA. (2015). Robust inverse-consistent affine CT-MR registration in MRI-assisted and MRI-alone prostate radiation therapy. Medical Image Analysis. 23(1), 56-69. https://doi.org/10.1016/j.media.2015.04.014
  9. Pagnozzi AM, Dowson N, Bourgeat P, Bradley AP, Boyd RN, Rose S (2015): Expectation-maximization with image-weighted Markov Random Fields to handle severe pathology. In: .Digital Image Computing: Techniques and Applications (DICTA). Adelaide, SA. pp 1–6. https://doi.org/10.1109/DICTA.2015.7371257
  10. Dowling, JA., Sun, J., Pichler, P., Rivest-Hénault, D., Ghose, S., Richardson, H, Wratten, C., Martin, J., Arm, J., Best, L., Chandra, SS., Fripp, J., Menk, FW., Greer, PB. (2015). Automatic Substitute Computed Tomography Generation and Contouring for Magnetic Resonance Imaging (MRI)-Alone External Beam Radiation Therapy From Standard MRI Sequences. Int J Radiat Oncol Biol Phys. 93(5):1144-53. https://doi.org/10.1016/j.ijrobp.2015.08.045