CAP5516-Medical Image Computing (SPRING 2019)

Assistant Professor  •  Imaging Scientist  •   •  Home  • 

Instructor: Prof. Ulas Bagci    

Class time: Monday/Wednesday 1.00pm-2.20 pm
Class location: Eng1 0383
Office hours: Monday/Wednesday 2.30-3.30 pm, in my office HEC221.

COURSE GOALS: Imaging science is experiencing tremendous growth in the US. The New York Times recently ranked biomedical jobs as the number one fastest growing career field in the nation and listed bio-medical imaging as a primary reason for the growth. Biomedical imaging and its analysis are fundamental to understanding, visualizing, and quantifying medical images in clinical applications. With the help of automated and quantitative image analysis techniques, disease diagnosis will be easier/faster and more accurate, and leading to significant development in medicine in general. The goal of this course is to help students develop skills in artificial intelligence and machine learning techniques applied to biomedical image analysis. The following topics will be covered:
PRE-REQUEST: Basic Probability/Statistics, Calculus, programming is optional for those from non-engineering background (software use will be fine for project).

GRADING:Assignments and the project should include explanatory/clear comments as well as a short report describing the approach, detailed analysis, and discussion/conclusion. RECOMMENDED BOOKS (optional) PROGRAMMING (for those who want to implement their projects from scratch)
Students are enocouraged to use ITK/VTK programming libraries in implementation of the programming assignments and project.
ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis.
The Visualization Toolkit (VTK) is an open-source, freely available software system for 3D computer graphics, image processing, and visualization. It consists of a C++ class library and several interpreted interface layers including Tcl/Tk, Java, and Python.

Python and/or C/C++ can call functions of ITK/VTK easily. Matlab can be used for assignments as well.
Following book (Python programming samples for computer viion tasks) is freely available.
Python for Computer Vision

COLLABORATION POLICY
Collaboration on assignments is encouraged at the level of sharing ideas and technical conversation only. Please write your own code. Students are expected to abide by UCF Golden Rule.

LECTURE NOTES

Useful papers to read: 

POTENTIAL INDIVIDUAL PROJECTS

PAPERS TO PRESENT

  1. A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning
  2. Capsules for Object Segmentation
  3. Joint solution for PET image segmentation, denoising, and partial volume correction
  4. CheXnet - Radiologist Level Pneumonia Detection  with Deep Learning (using x-rays)
  5. Deep Learning Assisted Diagnosis of Knee MRI
  6. U-Net for Biomedical Image Segmentation (Adnan Shiraj Rakin is assigned to present this paper)
  7. S4ND: Single Shot Single Scale Lung Nodule Detection
  8. CardiacNET: Segmenting Left Atrium and Proximal Pulmonary Veins from cardiac MRI
  9. Brain Tumor Segmentation with Deep Learning
  10. Deep Learning for Diabetic Retinopathy in Retinal Fundus Photographs
  11. DeepOrgan: Pancreas Segmentation from CT Scans (Ravi is assigned to present this paper)
  12. An Artificial Agent for Robust Image Registration
  13. Brain Tissue Segmentation with Deep Learning
  14. Segmenting and Translating Multi-Modal Medical Images with GAN (Milan Salem is assigned to present this paper)
  15. Anatomical Priors Based Unsupervised Biomedical Image Segmentation
  16. U-Net: Deep Learning Cell Counting, Detection, and Morphometry (Adnan will combine this paper with original U-Net to present both)
  17. Medical Image Synthesis for Data Augmentation and Anonymization using GAN
  18. Detecting Cancer Metastases GigaPixel Pathology Images
  19. Dermatologist Level Classification of Skin Cancer with Deep Learning
  20. CAD with DL Architecture: Applications in Breast Lesions in US Images and Pulmonary Nodules in CT Scans 
  21. Q-Space Deep Learning - Model Free Diffusion MRI
  22. First year development of infant brain functional networks
  23. Synthesizing CT images from MRI
  24. Small structure segmentation from head/neck CTs
  25. Discovering dynamic brain networks from big data at rest and task
  26. Hough-CNN: Deep Learning for Segmenting Deep Brain Structures in MRI and Ultrasound (Zhefu Cheng is assigned to present this paper)

Contact

[Ulas Bagci in 2015]
Ulas Bagci in 2009
Name: Ulas Bagci
Email:
URL: http://www.cs.ucf.edu/~bagci
Work number: (+1) 407-823-1047
Fax number: (+1) 407-823-0594
CRCV Assistant: Tonya LaPrarie
Mailing address: Dr. Ulas Bagci
Center for Research in Computer Vision (CRCV)
4328 Scorpius Street, HEC 221, UCF

Orlando, Florida 32816, USA.

Last updated December, 2018 by Ulas Bagci.