ELEC 444/6661 Medical Image Processing
|Instructor:||Dr. Hassan Rivaz|
|Guest lecturers:||Dr. Rupert Brooks (two lectures), Dr. Habib Benali (one lecture), Dr. Christophe Grova (one lecture), and Dr. Brandon Helfield (half a lecture)|
|Course description:||This course covers the principles and algorithms used in the processing and analysis of medical images. Topics include denoising, machine learning, image registration and similarity metrics. Image analysis methods on the most common medical imaging modalities (X-ray, MRI, CT, ultrasound) will be covered. Projects and assignments will provide students experience working with actual medical imaging data.|
|Prerequisites:||Undergraduate students: ELEC 364 or ELEC 342 Signals and Systems and a strong knowledge of linear algebra.|
|Credits:||Undergraduate course ELEC 444: 3 credits. Graduate course ELEC 6661: 4 credits. The graduate course requires carrying out a significantly more demanding project, which accounts for the extra credit.|
|Location & time:||MB S2.285, Thursday 12noon to 2:30PM|
|Text Book:||Medical Image Processing, 2014, By Wolfgang Birkfellner, CRC Press, ISBN: 978-1466555570.|
|Reference Book:||Medical Image Processing, 2009, By Geoff Dougherty, Cambridge University Press, ISBN: 9780521860857.|
|Assignments:||Three assignments + one final project (in MATAB or another language based on student preference). Marker: Mr. M Ashikuzzaman, email: rasel.ashik AT gmail.com|
|General POD:||Mr. Amir Pirhadi. Email: amirpirhadi73 ATsign gmail.com. Ask any course-related questions (except for deep learning) from Mr Pirhadi. Please contact Deep learning POD for deep learning questions (info below).|
|Lab time/location:||Mondays 12:30PM to 5:30PM starting from Monday Sept 23. Location: H854. It is not mandatory to attend lab sessions. No material is presented here, only questions (any questions about course) are answered. Note that for deep learning questions, you should not go to Mr. Pirhadi. Instead, you should contact the deep learning POD (info below).|
|Deep learning POD:||Mr. Sobhan Goudarzi. Email: s_oudarz ATsign ece.concordia.ca. Please contact Mr. Goudarzi only for deep learing questions and arrange a time to meet him at EV10.245.|
|Project:||All students (grad and ugrad) must present in the last class (Week 13). The project due date is on Friday Dec 6 at 11:59AM and should be emailed to rasel.ashik AT gmail.com.|
|Project for undergrads:||Undergrads registered in ELEC 444 have an option of implementing a paper or doing a project based on the RANSAC algorithms. The students who choose the RANSAC project don't need to write a report and should email their code by Friday Dec 6 at 11:59AM to the marker.|
|Undergraduate (3 credits)||Graduate (4 credits)|
|Assignments||Post date||Due date||Points|
|1. Matrix and image manupulation||Thursday Sept 12, 9AM||Tuesday Sept 24, 11:59PM||1 point|
|2. Detecting edges of brain MRI||Thursday Oct 17, 5PM||Tuesday Oct 29, 11:59PM||4.5 points|
|3. k-means segmentation of brain MRI||Thursday Oct 30, 9AM||Tuesday Nov 12, 11:59PM||4.5 points|
|1, Sep 5||Logistics, introduction to X-ray, CT and nuclear imaging|
|2, Sep 12||Introduction to ultrasound and Magnetic Resonance (MR) imaging, images in Matlab|
|3, Sep 19||Convolution, aliasing in medical images|
|4, Sep 26||Denoising techniques in medical imaging, edge detection in medical images|
|5, Oct 3||Neuroimaging and computational neuroscience|
|6, Monday Oct 7 from 9AM to 11:30AM at FG B070||Neuroimaging and EEG. Note: 9AM to 11:30AM on MONDAY at a different location|
|Oct 17||Machine learning in medical imaging|
|8, Oct 24||Midterm in class stating at 12noon (mark your calendar)|
|9, Oct 31||Introduction to medical image segmentation, RANSAC and k-means in medical imaging|
|10, Nov 7||From linear filters to deep learning|
|11, Nov 14||Convolutional neural networks (aka CNN or ConvNet)|
|12, Nov 21||Registration of medical images, similarity metrics, Computer vision and motion estimation in ultrasound elastography|
|13, Nov 28||Student presentations|
|Database of images possibly useful for projects:|
1) BrainWeb MRI link ,
2) Ultrasound RF data link ,
3) Ultrasound raw data link ,
4) Fetal ultrasound head circumfrence: paper and data ,
5) Ultrasound data for segmentation provided by the paper below:
Yap, Moi Hoon, Gerard Pons, Joan Marti, Sergi Ganau, Melcior Sentis, Reyer Zwiggelaar, Adrian K. Davison, and Robert Marti. "Automated breast ultrasound lesions detection using convolutional neural networks." IEEE journal of biomedical and health informatics 22, no. 4 (2018): 1218-1226.
6) Liver ultrasound: CLUST ,
7) 2D Echocardiography, cardiac ultrasound. Paper: Leclerc et al, Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography, IEEE TMI 2019. the CAMUS Database,
8) 3D MRI and ultrasound: the BITE Database, the RESECT Database
And paper, the RESECT Database download , and MICCAI CuRIOUS Challenge
9) Ultrasound and CT of liver tumors. SYSU datasets.
10) 3D MRI of Brain: BRATS.
11) 3D MRI of Brain: WMH Segmentation.
12) 3D MRI of Brain: OASIS.
13) 3D MRI: Lumbar muscle and vertebral bodies segmentation of chemical shift encoding- based water-fat MRI: the reference database MyoSegmenTUM spine, 2019
14) Chest CT from dir-lab .
15) Chest CT from National Lung Screening Trial .
16) Pancreas CT from TCIA Pancreas CT-82 .
17) CT and MRI of several different organs Medical Decathlon .
18) CT, chest x-ray, digital pathology etc. Cancer Data Access System .
19) Liver CT LiTS - Liver Tumor Segmentation Challenge .
20) Abdominal CT IEEE TMI's DenseVNet Multi-organ Segmentation on Abdominal CT .
21) 50 abdomen CT scans from colorectal cancer chemotherapy from synapse, Beyond the Cranial Vault .
22) SCR database: Segmentation in Chest Radiographs, isi.uu.nl.
23) Inbreast Digital mamography link .
24) Curated Breast Imaging Subset of DDSM link .
25) A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection, IEEE TMI 2020 link .