Table of Contents

lectures

Lectures

The following table contains the lecture plan. The Resources column is deliberately extensive, we will most likely not be able to talk about all the methods. Let me know if you have some preferences as to what methods you would like to talk about, I am open to suggestions.

I assume the knowledge of the simple image processing methods such as thresholding or linear filtering. I also assume a basic knowledge of neural networks and deep learning (see links below). However, I can of course briefly explain the basic concepts if you let me know.

The links to download the full text of the papers should work for you, although in some cases you need to be on the CTU network. If they do not work, let me know.

You are supposed to read the papers (at least go through them quickly) before the lecture to get the most of it. There will be a quick test (a multiple choice quiz) at the beginning of some of the lab sessions related to some of the papers discussed during the lectures.

The lectures will take place at 9:15 on Wednesdays in room KN:E-127. If you cannot come, you do not need to apologize, just go through the papers and the presentations.

The lecture plan is tentative and may be updated, so please check it regularly.

Number Date Topic Resources
1 25.9. Segmentation - active contours, level sets [1.1] Xu, Prince: ''Snakes, Shapes, and Gradient Vector Flow'', IEEE TIP 1998.
[1.2] Chan, Vese: ''Active contours without edges'', IEEE TIP 2001.
[1.3] Malladi et al.: ''Shape modeling with front propagation: a level set approach'', IEEE PAMI 1995.

Kybic, Krátký: Discrete curvature calculation for fast level set segmentation, ICIP 2009.
short presentation about active contours
a few slides on levelsets by N.Paragios and some examples by D. Cremers
partial recording from the lecture (sorry the first part is missing)
2 2.10. Segmentation - shape models, [2.1] Cootes et al.: Active Shape Models - Their Training and Applications, Computer Vision and Image Understanding, 1995.
[2.2] Cootes et al.: Active appearance models, IEEE PAMI 2001.
[2.3] Heimann, Meinzer: Statistical shape models for 3D medical image segmentation: A review, MIA 2009.

raw lecture slides
Chapter 10 of the Svoboda, Kybic, Hlavac book
recording of the lecture
3 9.10. Segmentations - superpixels, random walker, GraphCuts, graph search, normalized cuts (A.Shekhovtsov) [3.1] Achanta et al.: ''SLIC Superpixels Compared to State-of-the-Art Superpixel Methods'', IEEE PAMI 2012.
[3.2] Chen, Pan: ''A Survey of Graph Cuts/Graph Search Based Medical Image Segmentation'', IEEE Reviews in Biomedical Engineering, 2018.
[3.3] Grady, Leo: ''Random Walks for Image Segmentation'', IEEE PAMI 2006.
[3.4] Shi, Malik: ''Normalized Cuts and Image Segmentation'', IEEE PAMI 2000.

superpixel lecture notes
normalized cuts lecture notes
random walker lecture notes
4 16.10. Segmentation - texture, texture descriptors, textons [4.1] Leung, Malik: ''Representing and recognizing the visual appearance of materials using three-dimensional textons'', International Journal of Computer Vision, 2001.
[4.2] M. Unser: ''Texture classification and segmentation using wavelet frames'', IEEE TIP, 1995.
[4.3] Reyes-Aldasoro, Bhalerao: Volumetric Texture Segmentation by Discriminant Feature Selection and Multiresolution Classification, IEEE TMI, 2007.

Chapter 15 of the Šonka, Hlaváč, Boyle book and the companion Svoboda, Kybic, Hlavac book.
Radim Šára's old lecture on texture (in Czech).
Castellano et al.: ''Texture analysis of medical images'', Clinical Radiology, 2004.
Madabhushi, et al.: ''Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions'', IEEE TMI, 2003.
Noble, Boukerroui: ''Ultrasound image segmentation: A survey'', IEEE TMI, 2006.
Malik et al.: Textons, Contours and Regions: Cue Integration in Image Segmentation, ICCV, 1999.
Nava, Kybic: "Supertexton-based segmentation in early Drosophila oogenesis" , ICIP, 2015.
Lecture notes: textons, volumetric texture, waveletdescriptors.
5 23.10. Segmentation - CNN, U-net [5.1] Ronneberger et al.: U-Net: ''Convolutional Networks for Biomedical Image Segmentation'', MICCAI, 2015.
[5.2] Oktay et al.: ''Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation'', IEEE TMI, 2018.

Pereira et al.: ''Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images'', IEEE TMI, 2016.
Kamnitsas et al: ''Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation'', MIA, 2017.
Chung et al.: TeTrIS: ''Template Transformer Networks for Image Segmentation With Shape Priors'', IEEE TMI, 2019.
Ghavami et al.: ''Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration'', MIA, 2019.
Karel Zimmermann: Convolutional networks (lecture notes)
Lecture notes: CNNs, U-net , Anatomically constrained CNN.
6 30.10. Detection of cells and nuclei [6.1] Y. Al-Kofahi et al.: ''Improved automatic detection and segmentation of cell nuclei in histopathology images'', IEEE Trans. Biomed. Eng., 2010.
[6.2] Sirinukunwattana et al.: ''Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images'', IEEE TMI, 2016.
[6.3] Naylor: ''Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map'', IEEE TMI, 2019.

Irshan et al: ''Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential'', IEEE Reviews in Biomedical Engineering, 2013.
Lecture notes: Al-Kofahi et al., Sirinukunwattana et al., Naylor et al.
7 6.11. Detection of vessels and fibers [7.1] Frangi et al.: ''Multiscale vessel enhancement filtering'', LNCS, 1998.
[7.2] Türetken et al.: ''Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming'', IEEE PAMI, 2016.
[7.3] Türetken et al.: ''Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization with Geometric Priors'', Neurinformatics, 2011.
[7.4] Sironi: ''Multiscale Centerline Detection'', IEEE PAMI, 2016.

Lecture notes: vessel detection
8 13.11. Detection of nodules and mammographic lesions [8.1] Murphy et al.: ''A large scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification'', MIA, 2009.
[8.2] Setio et al.: ''Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks'', IEEE TMI, 2016.
[8.3] Kooi et al: ''Large scale deep learning for computer aided detection of mammographic lesion'', MIA, 2017.

Setio et al.. ''Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge'', MIA, 2017.
Lecture notes: nodules and mammography.
9 20.11. Localization of organs and structures [9.1] Sofka et al.: ''Automatic Detection and Measurement of Structures in Fetal Head Ultrasound Volumes Using Sequential Estimation and Integrated Detection Network (IDN)'', IEEE TMI, 2014.
[9.2] Xu et al.: ''Efficient Multiple Organ Localization in CT Image Using 3D Region Proposal Network'', IEEE TMI, 2019.
Lecture notes: localization.
10 27.11. Registration - ICP, coherent point drift, B-splines, rigid registration, multiresolution [10.1] Besl, McKay: ''A method for registration of 3D shapes'', IEEE PAMI, 1992.
[10.2] Myronenko, Song: ''Point Set Registration: Coherent Point Drift'', IEEE PAMI, 2010 .

Horn: ''Closed-form solution of absolute orientation using unit quaternions.'' J.Opt. Soc. Amer., 1987
Unser: ''Splines: a perfect fit for signal and image processing'', IEEE SPM, 1999.
Unser et al.: ''B-Spline Signal Processing: Part I—Theory'', ''Part II / Efficient Design and Applications''. IEEE TSP, 1993.
Lecture notes: ICP. Splines (M.Unser)
11 4.12. Registration - rigid, elastic, daemons [11.1] P. Thevenaz and M. Unser: ''“Optimization of mutual information for multiresolution image registration,'', IEEE TIP, 2000.
[11.2] J. Kybic and M. Unser: ''Fast Parametric Elastic Image Registration'', IEEE TIP, 2003.
[11.3] Thirion: ''Image matching as a diffusion process: an analogy with Maxwell’s demons'', Med. Im. Anal., 1998.

Sotiras, Paragios, Davatzikos: ''Deformable image registration: A Survey'', IEEE TMI 2013
Lecture notes: registration
12 11.12 Registration by optical flow and diffeomorpic methods [12.1] Bruhn et al: ''Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods'', IJCV, 2005.
[12.2] Avants et al.: ''Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain'', MIA, 2009.

Ashburner: ''A fast diffeomorphic image registration algorithm'', Neuroimage, 2007.
Lecture notes: optical flow, diffeomorphic registration
13 18.12. Registration by CNN [13.1] Balakrishnan et al.: ''VoxelMorph: A Learning Framework for Deformable Medical Image Registration'', IEEE TMI, 2019.
[13.2] X. Yang, R. Kwitt, M. Styner, and M. Niethammer: ''Quicksilver: Fast predictive image registration-A deep learning approach'', NeuroImage, 2017.

Dosovitskiy et al: FlowNet: Learning Optical Flow with Convolutional Networks, ICCV 2015
Lecture notes Voxelmorph and Quicksilver.
14 8.1. student project presentations

Other resources

Medical image analysis is a dynamic domain, recent and advanced algorithms are published mainly in the primary literature, i.e. scientific journals. The most relevant are

  • IEEE Transactions on Medical Imaging
  • IEEE Transactions on Biomedical Engineering
  • Medical Image Analysis
  • IEEE Transactions on Image Processing
  • International Journal on Computer Vision
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

These journals can be accessed free of charge thanks to a CTU subscription. Ask the central library in case of problems.

Books

  • Frangi, Prince, Sonka: Medical Image Analysis, Elsevier, 2023. The most recent, very comprehensive book. I have one copy.
  • Sonka, Hlavac, Boyle: Image Processing, Analysis and Machine Vision. Cengage Learning, 2015. This book is good for classical algorithms, can be borrowed from the Department of Cybernetics library.) There is also a companion book with Matlab implementation of some of the algorithms.
  • Toennies: Guide to Medical Image Analysis, Springer 2012
  • Deserno: ''Fundamentals of Biomedical Image Processing'', Springer 2011
  • Yoo: Insight into images. Taylor & Francis, 2004
  • Birkfellner: Applied Medical Image Processing, CRC Press 2011
  • Jan: Medical Image Processing, Reconstruction and Restoration, CRC Press 2006
  • Dhawan: Medical Image Analysis, IEEE Press, 2003
  • Šonka, Fitzpatrick: Handbook of Medical Imaging: Volume 2, Medical Image Processing and Analysis, SPIE Press, 2000.

Deep learning resources

courses/zmo/lectures/start.txt · Last modified: 2024/10/15 11:48 by kybicjan