The Cancer Imaging Archive (TCIA) Public Access

Space shortcuts

Child pages
  • Publications

Versions Compared

Old Version 173

changes.mady.by.user Sean Berryman

Saved on

compared with

New Version 174

changes.mady.by.user Sean Berryman

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

  1. Stoll M, Stoiber EM, Grimm S, Debus J, Bendl R, Giske K. Comparison of Safety Margin Generation Concepts in Image Guided Radiotherapy to Account for Daily Head and Neck Pose Variations. PloS one. 2016;11(12):e0168916. DOI: 10.1371/journal.pone.0168916
  2. Ahmadvand P, Duggan N, Bénard F, Hamarneh G. Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface. MLMI 2016 in conjunction with the 19th Int'l Conference on MICCAI. (link)
  3. Fedorov A, Clunie D, Ulrich E, et al. (2016) DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ 4:e2057 DOI: 10.7717/peerj.2057
  4. Beichel RR, Van Tol M, Ulrich EJ, et al. (2016) Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach. Medical Physics 43:2948–2964. DOI: 10.1118/1.4948679.

Collection: QIN Prostate

  1. Lavasani, S. N., Mostaar, A., & Ashtiyani, M. (2017). Automatic prostate cancer segmentation using kinetic analysis in dynamic contrast-enhanced MRI. Journal of Biomedical Phsyics and Engineering.  doi: 10.22086/jbpe.v0i0.555 
  2. Fedorov A, Fluckiger J, Ayers GD, Li X, Gupta SN, Tempany C, Mulkern R, Yankeelov TE, Fennessy FM. A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation. Magnetic resonance imaging. 2014;32(4):321-9.
  3. Hegde JV, Mulkern RV, Panych LP, Fennessy FM, Fedorov A, Maier SE, Tempany C. Multiparametric MRI of prostate cancer: An update on state‐of‐the‐art techniques and their performance in detecting and localizing prostate cancer. Journal of Magnetic Resonance Imaging. 2013;37(5):1035-54.
  4. Benalcázar, M. E., M. Brun, et al. (2015). Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014, Springer.

  5. Li, A., C. Li, et al. (2013). Automated Segmentation of Prostate MR Images Using Prior Knowledge Enhanced Random Walker. Digital Image Computing: Techniques and Applications (DICTA), 2013 International Conference on, IEEE.

  6. Qiu, W., J. Yuan, et al. (2014). Prostate segmentation: An efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images. Medical Imaging, IEEE Transactions on 33(4): 947-960.

  7. Xie, Q. and D. Ruan (2014). Low-complexity atlas-based prostate segmentation by combining global, regional, and local metrics. Medical physics 41(4): 041909.

  8. Zhao, T. and D. Ruan (2015). Two-stage fusion set selection in multi-atlas-based image segmentation. Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, IEEE.

...