| Announcements | Medical Image Analysis | Quick Links |
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Coming up again in Spring 2016 in an OBE* format!
*OBE is outcome-based education according to Washington Accord |
EE61008, Spring 2016
Subject Type: Elective | LTP: 3-1-0 | Credits: 4 Location: N232, Electrical Engineering Time: Slot U (Mon. 3-5 PM, Tue. 2-4 PM) Instructor: Dr. Debdoot Sheet Tutoring: N240, SIP Lab, Electrical Engineering Niladri Garai, Rachana Sathish, Kanithi Praveen Kumar |
Link to list of detailed reading texts.
Grand Challenges in Biomedical Image Analysis Tools of the Trade: MeVisLab SDK | Enthough Canopy Python | MedPy | MikTex Latex compiler | Git version control Tutoring Group on Piazza |
| Why this subject? | |
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You get to learn about current technology in processing and analysis of medical images; a rapidly growing industry expected to reach $2.4 billion by 2017. If you are looking forward to a career in medical imaging instrument and softwares design, medical imaging, medical visualization, medical robotics and augmented reality, this is the key subject you should enroll for.
The best parts just do not end here. A mix of hands-on tutorials coupled with lectures provide you with a balanced exposure to nuances of medical image analysis. Text books and reading materials are completely electronic, online and available as web-browser and smatphone apps. Prerequisite: Digital Image Processing Measure of Outcome: A student undertaking this subject would be graded based on perfromance in all of the following: (1) Regular participation in class activity. (2) Compete in an active GrandChallenges in Bio-MedIA (in a group of 2-3). If you make up to the Top-3 teams on the GrandChallenge, you claim the Hall-of-Fame. (3) Make a mid-term proposal presentation for the GrandChallenge. (4) Make an end-term presentation of the solution. Submit a 4-page term paper on the solution. (5) Appear for all the exams. |
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| Random Walks
Random walks for image segmentation. | Article: IEEE Trans. Pattern Analysis and Machine Intelligence Random walks for ultrasound confidence maps. | Article: Medical Image Analysis |
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| Decision Trees and Random Forests
Decision trees and random forests for medical image analysis. | Book: Springer |
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| Deep Neural Networks
Deep learning. | Article: Nature | Toolbox: GitHub | Forum: DeepLearning |
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| Evaluation and Validation
Systematic evaluations and ground truth. | Article: Biomedical Image Processing |
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| Computed Tomography
Physics of imaging. | Article: Medical Imaging Technology | Article: Principles of Computerized Tomographic Imaging Automatic (and semi-automatic) segmentation of blood vessels in the lungs from CT images. VESSEL12 | Article: Medical Image Analysis |
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| Magnetic Resonance Imaging
Physics of imaging. | Article: Medical Imaging Technology Multi-class classification of patients with Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy controls (CN) using multi-center structural MRI data. CAD Dementia Challenge 2014 | Article: NeuroImage |
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| Ultrasonic Imaging
Physics of imaging. | Article: Texas Instruments White Paper Segmentation of anatomical structures to measure obsteatric biometric parameters from 2D fetal ultrasound images. Challenge US 2012 | Article: IEEE Trans. Med. Imaging |
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| Digital Pathology
Optical microscopy. | Article: Encyclopedia of Imaging Science and Technology (Semi-)automatic mitotic figure detection methods on regions extracted from whole-slide pathology images. AMIDA13 | Article: Medical Image Analysis |
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| Phase-Contrast Microscopy
Manually tracking cells is an extremely laborious task, due to the large amount of image data acquired during live-cell studies. This challenge compares automatic approaches for cell tracking. Cell Tracking Challenge 2013 | Article: Bioinformatics |
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| Fluorescence and Non-linear Microscopy
Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks. Particle Tracking Challenge 2012 | Article: Nature Methods |
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| Retinal Imaging
Computer aided detection and diagnosis (CAD) of diabetic retinopathy. Retinopathy Online Challenge 2009 | Article: IEEE Trans. Med. Imaging |
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| Tissue Characterization for In situ Histology
Ultrasonic tissue characterization. | Article: Medical Image Analysis OCT tissue characterization. | Article: Journal of Biomedical Optics |
| Conferences | Int. Symp. Biomed. Imaging (ISBI), Med. Image Comput., Comp. Assist. Interv. (MICCAI), SPIE Med. Imaging, Int. Conf. Image Processing (ICIP), Computer Vision and Pattern Recognition (CVPR), Int. Conf. Comput. Vis. (ICCV), Eur. Conf. Comput. Vis. (ECCV), Asian Conf. Comput. Vis. (ACCV), ACM SIGGRAPH, ACM SIGGRAPH-Asia, Indian Conf. Vis., Graph., Image Process. (ICVGIP), Nat. Conf. Vis., Pat. Recog., Image Process., Graph. (NCVPRIPG) |
| Journals | Medical Image Analysis (MedIA), IEEE Trans. Med. Imaging (TMI), IEEE J. Biomed. Health Inf. (JBHI), IEEE Trans. Biomed. Engg. (TBME), SPIE J. Med. Imaging (JMI), Comput. Med. Imag., Graph. (CMIG), IEEE Trans. Image Process. (TIP), IEEE Trans. Pat. Anal., Mach. Intell. (T-PAMI), Int. J. Comput. Vis. (IJCV), IET Image Process. (IET-IP), ACM Trans. Graphics (TOG) |
| BioDigital Human | Web browser app that can be handy while understanding human anatomy for medical image analysis |
| Android Studio and SDK | Developer studio, IDE and SDK if you are interested to develop Android apps as part of your mini-project. |
| MicroDicom viewer | Handy software utility to quickly view DICOM files |
| Challenges @ ISBI 2016 | Collection of challenges in medical image analysis hosted at the International Symposium on Biomedical Imaging 2016, Prague, Czech Republic. |
| Mammographic Image Analysis | Collection of mammography images provided by Mammographic Image Analysis Society (MIAS). |
| DRIVE: Digital Retinal Images for Vessel Extraction | A standard dataset of 40 color fundus images for comparative studies on segmentation of blood vessels in retinal images. |
| Drishti-GS Dataset | Dataset of color fundus retinal images for Glaucoma detection. |
| Farsiu's Dataset @ Duke Univ. | Collection of multiple datasets of ophthalmic OCT speckle images for tissue characterization, denoising, super-resolution and mosaicing. |
| MIT OCW Linear Algebra | Revise your Linear Algebra with video lectures and resources by Prof. Gilbert Strang. |
| Products / Labs / Companies |
| Coming up! |
| Coming up! |
| The aim is to teach students advanced technology in processing and analysis of medical images. It would be beneficial to students opting for specialization in medical imaging instrument design, medical imaging, medical visualization, medical robotics and augmented reality, who can use the gained skills in order to develop newer technological innovations and regularize their high-throughput clinical translation and usage. | On completion of the course, a student would be able to:
1. Explain and discuss the scientific principles of medial image formation, tissue energy interaction as basis for different imaging modalities, inter-relation between image appearance and tissue properties, quantitative medicine, multi-scale and multi-resolution relation in medical diagnostics, medical visualization and augmented reality. 2. Demonstrate the ability of analyzing medical images using their foundations of linear algebra, graph based and machine learning based approaches. 3. Design and develop new techniques for improving clinical workflow with incorporation of medical image analysis in regular clinical usage. |