Medical Image Analysis

Fundus Image Segmentation

Goal

You have to read papers, complete the code of the paper, and write a report.

Related papers

You can choose at least one of the following papers.

  • (MNet) Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu, and Xiaochun Cao, "Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation", IEEE Transactions on Medical Imaging, vol. 37, no. 7, pp. 1597-1605, 2018. [TensorFlow 1.14 + Keras) + Matlab] [PDF]
  • (DENet) Huazhu Fu, et al., "Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image," IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2493-2501, Nov. 2018. [Keras/Tensorflow] [PDF]

OCT Layer Segmentation

Goal

You have to read papers, complete the code of the paper, and write a report.

Related papers

You can choose at least one of the following papers.

  • S. Motamedi, et al., "Normative Data and Minimally Detectable Change for Inner Retinal Layer Thicknesses Using a Semi-automated OCT Image Segmentation Pipeline," Frontiers in Neurology, 25 November 2019. URL. SAMIRIX: Matlab @ GitHub, NeuroDIal @ GitHib for OCT analysis. [PDF]
  • A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, J. L. Prince, "Retinal layer segmentation of macular OCT images using boundary classification." Biomedical Optics Express 4, 1133-1152, 2013. OCTLayerSegmentation by AURA Tools on NITRC [PDF]

Mammogram breast cancer detection / classification

Goal

You have to read papers, complete the code of the paper, and write a report.

Related papers

You can choose at least one of the following papers.

  • McKinney, Scott Mayer, et al. "International evaluation of an AI system for breast cancer screening." Nature 577.7788 (2020): 89-94. (paper)
  • Shen, Li, et al. "Deep learning to improve breast cancer detection on screening mammography." Scientific reports 9.1 (2019): 1-12. (paper) (python source code @ GitHub)
  • Automatic mass detection in mammograms using deep convolutional neural networks, Journal of Medical Imaging 6.3 (2019). (paper)

Dataset

CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography)

  • Official website: CBIS-DDSM (you can download the dataset here or download from following share point)
  • Google Drive for download:
    • classification - Mass Training + Mass Test dicom file (50GB) (not finished)
    • ROI for detection – to be uploaded
    • python file provided by Lai (2.3GB)