CAP5516-Medical Image Computing (SPRING 2019)
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.
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:
Basic Probability/Statistics, Calculus, programming is optional for those from non-engineering background (software use will be fine for project).
- Basics of Radiological Image Modalities and their clinical use
- Introduction to Medical Image Computing and Toolkits
- Medical Image Computing Before and After Deep Learning Era
- Medical Image Registration, Segmentation, Visualization
- Machine Learning/Deep Learning in Medical Imaging
- NeuroImaging: fMRI, DTI, MRI, Connectome
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)
- Midterm 30%
- Paper presentation/discussion 20%
- Project 50%
PROGRAMMING (for those who want to implement their projects from scratch)
- Goodfellow, Bengio, Courville, Deep Learning, 2016.
- Suetens, P. Fundamentals of Medical Imaging, Cambridge University Press
- Prince, J. & Links, J. Medical Imaging Signals and Systems, Prentice Hall,
- Bankman, Isaac.Handbook of Medical Imaging: Processing and Analysis, Academic Press,
- Yoo, Terry S. Insight into Images: Principles and Practice for Segmentation, Registration and Image Analysis, CRC Press,
- Sethian, J.A., Level-set Methods, Cambridge University Press, 2000,
- Duda, Hart and Stork, Pattern Classification (2nd Edition), Wiley, 2000,
- Koller and Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009,
- Strang, Gilbert. Linear Algebra and Its Applications 2/e, Academic Press, 1980.
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 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.
Useful papers to read:
POTENTIAL INDIVIDUAL PROJECTS
- Several projects are available from https://grand-challenge.org/challenges/
- Try to choose something from MICCAI Challenges
- Lung Lobe Segmentation from CT Scans (Use LOLA11 Segmentation Challenge Data Set)
- Segmentation of Knee Images from MRI (Use SKI 2010 Data Set))
- Multimodal Brain Tumor Segmentation (Use BraTS Data Set)
- Automatic Lung Nodule (cancer) Detection (Use LUNA Data Set)
- Automatically measure end-systolic and end-diastolic volumes in cardiac MRIs. (Use Kaggle Cardiac Data Set)
- Head-Neck Auto Segmentation Challenge (Use MICCAI 2015 Segmentation Challange Data Set)
- CAD of Dementia from Structural MRI (Use MICCAI 2014 Segmentation Challenge Data Set)
- DTI Tractography Challenge (Use MICCAI 2014 Segmentation Challenge Data Set)
- EMPIRE 2010 - Pulmonary Image Registration Challenge (http://empire10.isi.uu.nl/index.php, I have the team name and password for downloading the data set).
- MACHINE LEARNING Challenge in medical imaging )
PAPERS TO PRESENT
- A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning
- Capsules for Object Segmentation
- Joint solution for PET image segmentation, denoising, and partial volume correction
- CheXnet - Radiologist Level Pneumonia Detection with Deep Learning (using x-rays)
- Deep Learning Assisted Diagnosis of Knee MRI
- U-Net for Biomedical Image Segmentation (Adnan Shiraj Rakin is assigned to present this paper)
- S4ND: Single Shot Single Scale Lung Nodule Detection
- CardiacNET: Segmenting Left Atrium and Proximal Pulmonary Veins from cardiac MRI
- Brain Tumor Segmentation with Deep Learning
- Deep Learning for Diabetic Retinopathy in Retinal Fundus Photographs
- DeepOrgan: Pancreas Segmentation from CT Scans (Ravi is assigned to present this paper)
- An Artificial Agent for Robust Image Registration
- Brain Tissue Segmentation with Deep Learning
- Segmenting and Translating Multi-Modal Medical Images with GAN (Milan Salem is assigned to present this paper)
- Anatomical Priors Based Unsupervised Biomedical Image Segmentation
- U-Net: Deep Learning Cell Counting, Detection, and Morphometry (Adnan will combine this paper with original U-Net to present both)
- Medical Image Synthesis for Data Augmentation and Anonymization using GAN
- Detecting Cancer Metastases GigaPixel Pathology Images
- Dermatologist Level Classification of Skin Cancer with Deep Learning
- CAD with DL Architecture: Applications in Breast Lesions in US Images and Pulmonary Nodules in CT Scans
- Q-Space Deep Learning - Model Free Diffusion MRI
- First year development of infant brain functional networks
- Synthesizing CT images from MRI
- Small structure segmentation from head/neck CTs
- Discovering dynamic brain networks from big data at rest and task
- Hough-CNN: Deep Learning for Segmenting Deep Brain Structures in MRI and Ultrasound (Zhefu Cheng is assigned to present this paper)
| 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.