====== Přednášky ======
V následující tabulce najdete navržený seznam přednášek. Je to návrh, pravděpodobně bude ještě mírně upravován. Seznam zdrojů (sloupec //Resources//) je záměrně extenzivní, velmi pravděpodobně nestihneme probrat všechny popsané metody. Články, které budou probrány podrobněji a jejichž znalost bude vyžadována, budou označeny a budou k dispozici v systému [[https://perusall.com/|Perusall]]. Vzhledem k malému počtu studentů je zde poměrně značný prostor pro úpravy obsahu, neváhejte se na mne obrátit, pokud máte o nějaké téma zvláštní zájem nebo pokud je pro vás naopak nezajímavé. Oproti původnímu návrh se například nevešlo téma rekonstrukce (např. pro CT, PET atp.)

Předpokládám znalost metod popsaných v předmětu [[https://cw.fel.cvut.cz/old/courses/ae4m33dzo/start|DZO]], jako jsou např. jednoduché segmentační algoritmy (prahování, k-means, atp.) V případě velkého zájmu je ovšem stručně zopakovat můžeme. Taktéž předpokládám alespoň povšechnou znalost principů neuronových sítí a hlubokého učení (viz. též odkazy níže), ale ani tady není problém stručný úvod udělat.




| Number |  Date  | Topic |  Resources | 
| 1 | 23.9. | Segmentation - active contours, level sets  |Xu, Prince: [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.48.2963&rep=rep1&type=pdf|''Snakes, Shapes, and Gradient Vector Flow'']]. IEEE TIP 1998; Chan, Vese [[https://ieeexplore.ieee.org/document/902291|''Active contours without edges'']], IEEE TIP 2001. Malladi et al [[https://ieeexplore.ieee.org/document/368173|''Shape modeling with front propagation: a level set approach.'']], IEEE PAMI 1995.  //Nepovinné články:// <fc #909090>Kybic, Krátký. [[ftp://cmp.felk.cvut.cz/pub/cmp/articles/kratky/Kratky-SPIE2008.pdf|Discrete curvature calculation for fast level set segmentation.]] ICIP 2009.</fc>. {{:courses:bam33zmo:lectures:snakes.pdf |short presentation about active contours}}, a few slides on {{:courses:bam33zmo:lectures:paragios_levelsets.pdf |levelsets}} by N.Paragios and some {{:courses:bam33zmo:lectures:cremers_levelsets.pdf|examples}} by D. Cremers, [[https://bbb04.felk.cvut.cz//playback/presentation/2.0/playback.html?meetingId=d080de493d3c9b46c543e17300adbdbee348dd84-1600842601808|partial recording from the lecture]] (sorry the first part is missing) |
| 2 | 30.9. | Segmentation - shape models, | Cootes et al: [[https://pdfs.semanticscholar.org/f731/b6745d829241941307c3ebf163e90e200318.pdf|Active Shape Models - Their Training and Applications]], Computer Vision and Image Understanding. 1995. Cootes et al: [[https://people.eecs.berkeley.edu/~efros/courses/AP06/Papers/cootes-pami-01.pdf|Active appearance models.]] IEEE PAMI 2001. Heimann, Meinzer: [[https://www.sciencedirect.com/science/article/pii/S1361841509000425|Statistical shape models for 3D medical image segmentation: A review.]] MIA 2009. {{ :courses:bam33zmo:lectures:shape_models.pdf |raw lecture slides}}, {{ :courses:bam33zmo:lectures:10underst.pdf | Chapter 10 of the Svoboda, Kybic, Hlavac book}} [[https://bbb04.felk.cvut.cz//playback/presentation/2.0/playback.html?meetingId=63d26c943c19154f42bd10594475ecade12ede4d-1601447401257|recording of the lecture]] | 
| 3  |   7.10.  | Segmentations - superpixels, random walker, GraphCuts, graph search, normalized cuts | Achanta et al.: SLIC [[https://ieeexplore.ieee.org/abstract/document/6205760|''Superpixels Compared to State-of-the-Art Superpixel Methods'']]. IEEE PAMI 2012. Chen, Pan: [[https://ieeexplore.ieee.org/abstract/document/8270593|''A Survey of Graph Cuts/Graph Search Based Medical Image Segmentation'']]. IEEE Reviews in Biomedical Engineering. 2018 Grady: [[https://ieeexplore.ieee.org/document/1704833 |''Random Walks for Image Segmentation'']], IEEE PAMI 2006. Shi, Malik: [[https://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf |''Normalized Cuts and Image Segmentation,'']] IEEE PAMI 2000. {{ :courses:bam33zmo:lectures:superpixels.pdf |superpixel lecture notes}}, {{ :courses:bam33zmo:lectures:normalizedcuts.pdf |normalized cuts lecture notes}} {{ :courses:bam33zmo:lectures:randomwalker.pdf |random walker lecture notes}}|
| 4 | 14.10. <fc #ff0000>11:00</fc> | Segmentation - texture, texture descriptors, textons | Leung, Malik. [[https://people.eecs.berkeley.edu/~malik/papers/LM-3dtexton.pdf |''Representing and recognizing the visual appearance of materials using three-dimensional textons'']]. International Journal of Computer Vision, 2001. M. Unser: [[https://ieeexplore.ieee.org/document/469936 |''Texture classification and segmentation using wavelet frames'']], IEEE TIP. 1995   Reyes-Aldasoro, Bhalerao, [[https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4039538|Volumetric Texture Segmentation by Discriminant Feature Selection and Multiresolution Classification]], IEEE TMI 2007. //Optional://   Chapter 15 of the {{ :courses:bam33zmo:lectures:shb15_texture.pdf |Šonka, Hlaváč, Boyle book}} and the companion {{ :courses:bam33zmo:lectures:skh15_texture.pdf |Svoboda, Kybic, Hlavac book}}. Radim Šára's {{ :courses:bam33zmo:lectures:lecture_texture_sara.pdf |old lecture on texture}} (in Czech).  Castellano et al: [[https://www.clinicalradiologyonline.net/article/S0009-9260(04)00265-X/pdf |''Texture analysis of medical images'']]. Clinical Radiology 2004. Madabhushi, et al. [[https://ieeexplore.ieee.org/document/1194626 |''Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions'']]. IEEE TMI 2003. Noble, Boukerroui: [[https://ieeexplore.ieee.org/abstract/document/1661695 |''Ultrasound image segmentation: A survey.'']] IEEE TMI 2006. Malik et al. [[https://www.cis.upenn.edu/~jshi/papers/ICCV99b_final.pdf|Textons, Contours and Regions: Cue Integration in Image Segmentation]] ICCV1999, Nava, Kybic [[ ftp://cmp.felk.cvut.cz/pub/cmp/articles/kybic/Nava-ICIP2015.pdf|"Supertexton-based segmentation in early Drosophila oogenesis." ]], ICIP2015. **Lecture notes:** {{ :courses:bam33zmo:lectures:textons.pdf |textons}}, {{ :courses:bam33zmo:lectures:volumetrictexture.pdf |volumetric texture}}, {{ :courses:bam33zmo:lectures:waveletdescriptors.pdf |waveletdescriptors.}} | 
| 5 | 21.10. | Segmentation - CNN, U-net | Ronneberger et al: U-Net: [[https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/ |''Convolutional Networks for Biomedical Image Segmentation'']]. MICCAI 2015. Oktay et al: [[https://ieeexplore.ieee.org/document/8051114 |''Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation.'']] IEEE TMI 2018. //Optional:// Pereira et al: [[https://ieeexplore.ieee.org/document/7426413 |''Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images'']], IEEE TMI 2016 Kamnitsas et al: [[https://arxiv.org/abs/1603.05959 |''Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation'']], MIA 2017 (741 citations) Chung et al: TeTrIS: [[https://ieeexplore.ieee.org/document/8672808 |''Template Transformer Networks for Image Segmentation With Shape Priors'']]. IEEE TMI 2019 Ghavami et al: [[https://www.sciencedirect.com/science/article/pii/S1361841519301008 |''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. **Lecture notes:** {{ :courses:bam33zmo:lectures:cnn_intro.pdf |CNNs,}} {{ :courses:bam33zmo:lectures:unet.pdf |U-net, }} {{ :courses:bam33zmo:lectures:anatomicalcnns.pdf |Anatomically constrained CNN}} | 
|  | 28.10. | //holidays// | | 
| 6 | 4.11. | Detection of cells and nuclei | Y. Al-Kofahi et al, “[[https://ieeexplore.ieee.org/document/5306149 |''Improved automatic detection and segmentation of cell nuclei in histopathology images'']],” IEEE Trans. Biomed. Eng., vol. 57, no. 4, pp. 841–852, Apr. 2010  Sirinukunwattana et al: [[https://ieeexplore.ieee.org/document/7399414 |''Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images'']]. IEEE TMI 2016 Naylor: [[https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8438559 |''Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map'']]. //Optional:// IEEE TMI 2019 Irshan et al: [[https://ieeexplore.ieee.org/document/6690201 |''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:** {{ :courses:bam33zmo:lectures:alkofahi.pdf |Al-Kofahi et al.}}, {{ :courses:bam33zmo:lectures:locality_sensitive.pdf | Sirinukunwattana et al.}}, {{ :courses:bam33zmo:lectures:deep_regression.pdf | Naylor et al.}} |
| 7 | 11.11. | Detection of vessels and fibers | Frangi et al: [[https://link.springer.com/content/pdf/10.1007%2FBFb0056195.pdf |''Multiscale vessel enhancement filtering'']]. LNCS 1998 Türetken et al: [[https://ieeexplore.ieee.org/document/7405348 |''Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming'']]. IEEE PAMI 2016 Türetken et al:[[https://link.springer.com/article/10.1007/s12021-011-9122-1 |''Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization with Geometric Prior'']]s. Neurinformatics. 2011 Sironi: [[https://ieeexplore.ieee.org/document/7172549 |''Multiscale Centerline Detection'']], IEEE PAMI 2016. **Lecture notes:** {{ :courses:bam33zmo:lectures:vessel_detection.pdf |vessel detection}} |
| 8 | 18.11. | Detection of nodules and mammographic lesions | Murphy et al: [[https://www.sciencedirect.com/science/article/pii/S1361841509000516 |''A large scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification'']]. MIA 2009 Setio et al: [[https://ieeexplore.ieee.org/document/7422783 |''Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks.'']] IEEE TMI 2016 Kooi et al: [[https://geertlitjens.nl/publication/kooi-17/kooi-17.pdf |''Large scale deep learning for computer aided detection of mammographic lesion.'']] MIA, 2017. //Optional:// Setio et al. [[https://www.sciencedirect.com/science/article/pii/S1361841517301020 |''Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.'']] MIA 2017. **{{ :courses:bam33zmo:lectures:nodules.pdf |Lecture notes}}**  |
| 9 | 25.11. | Localization of organs and structures | Sofka el al [[https://msofka.github.io/pdfs/sofka-tmi14.pdf |''Automatic Detection and Measurement of Structures in Fetal Head Ultrasound Volumes Using Sequential Estimation and Integrated Detection Network (IDN)'']]. IEEE TMI 2014 Xu et al: [[https://ieeexplore.ieee.org/document/8625393 |''Efficient Multiple Organ Localization in CT Image Using 3D Region Proposal Network.'']] IEEE TMI 2019. **{{ :courses:bam33zmo:lectures:localization.pdf |lecture notes}}** |
| 10 | 2.12. | Registration - ICP, coherent point drift, B-splines, rigid registration, multiresolution | Besl, McKay: [[https://ieeexplore.ieee.org/document/121791 |''A method for registration of 3D shapes'']]. IEEE PAMI. 1992 Myronenko, Song: Point Set Registration: [[https://ieeexplore.ieee.org/document/5432191 |''Coherent Point Drift'']]. IEEE PAMI, 2010 **{{ :courses:bam33zmo:lectures:icp.pdf |Lecture notes.}}** //Optional:// Unser: [[http://bigwww.epfl.ch/publications/unser9902.pdf |''Splines: a perfect fit for signal and image processing'']], IEEE SPM, 1999 Unser et al: [[http://bigwww.epfl.ch/publications/unser9301.html |''B-Spline Signal Processing: Part I—Theory'']]. [[http://bigwww.epfl.ch/publications/unser9302.html |''Part II / Efficient Design and Applications'']]. IEEE TSP. 1993 |
| 11 | 9.12. | Registration - rigid, elastic, daemons | P. Thevenaz and M. Unser, [[https://ieeexplore.ieee.org/document/887976 |''“Optimization of mutual information for multiresolution image registration,'']] IEEE TIP 2000. Jan Kybic and Michael Unser [[https://ieeexplore.ieee.org/document/1240109 |''Fast Parametric Elastic Image Registration.'']] IEEE TIP. 2003 Thirion: [[https://hal.inria.fr/inria-00615088/document |''Image matching as a diffusion process: an analogy with Maxwell’s demons.'']] Med. Im. Anal. 1998. **{{ :courses:bam33zmo:lectures:registration.pdf |lecture notes}}**  //Optional:// Sotiras, Paragios, Davatzikos: [[https://hal.inria.fr/hal-00684715v3/document |''Deformable image registration: A Survey'']], IEEE TMI 2013 |
| 12 | 16.12. | Registration by CNN | Balakrishnan et al: [[https://arxiv.org/pdf/1809.05231.pdf |''VoxelMorph: A Learning Framework for Deformable Medical Image Registration'']]. IEEE TMI 2019 X. Yang, R. Kwitt, M. Styner, and M. Niethammer, “Quicksilver: [[https://arxiv.org/abs/1703.10908 |''Fast predictive image registration-A deep learning approach,'']]” NeuroImage, 2017. **Lecture notes** {{ :courses:bam33zmo:lectures:voxelmorph.pdf |Voxelmorph}} and {{ :courses:bam33zmo:lectures:registration_quick.pdf |Quicksilver}}. //Optional:// Sun et al. PWC-Net: [[https://openaccess.thecvf.com/content_cvpr_2018/papers/Sun_PWC-Net_CNNs_for_CVPR_2018_paper.pdf |''CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume.'']] CVPR 2018  |
| 13 | 6.1. |  | //student project presentations// - to be confirmed |

/* Registration - optical flow, diffeomorphic methods | Bruhn et al: [[https://link.springer.com/article/10.1023/B:VISI.0000045324.43199.43 |''Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods.'']] IJCV 2005 Ashburner: [[http://sciencedirect.com/science/article/pii/S1053811907005848 |''A fast diffeomorphic image registration algorithm'']]. Neuroimage 2007 (&gt;4000 citations) Avants et al: [[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2276735/ |''Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.'']] MIA 2009 |
*/

/* | 14 |     | //student project presentations // instead of 6.1. - to be confirmed | | */
/* | | 6.1. | //no lecture// - to be confirmed | |  */


== Deep learning (odkazy) ==

  * [[https://cw.fel.cvut.cz/b201/courses/b3b33vir/start|''Vision for robots'']] course has very nice slides about deep learning for image processing.
  * [[https://pytorch.org/tutorials/|''Pytorch tutorials'']]
  * If you want to know everything, read the [[https://www.deeplearningbook.org/|''Deep learning book'']]


== Literatura a zdroje ==

Jedná se o velmi rychle se vyvíjející oblast, pokročilejší algoritmy najdete zejména v primární literatuře, tedy v časopisech, z nichž nejvíce relevantní jsou

   * 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

k těmto časopisům máte v rámci ČVUT elektronický přístup zdarma. Případné dotazy směřujte na [[http://knihovna.cvut.cz/|Ústřední knihovnu ČVUT]].

Užitečné však mohou být i knihy, jako například tyto:

   * Toennies: Guide to Medical Image Analysis, Springer 2012
   * Deserno: [[https://elearning.uniroma1.it/pluginfile.php/509402/mod_resource/content/1/9783642158155-c1.pdf|''Fundamentals of Biomedical Image Processing'']], Springer 2011
   * Yoo: Insight into images. Taylor &amp; 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.