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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 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 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: ''Snakes, Shapes, and Gradient Vector Flow''. IEEE TIP 1998; Chan, Vese ''Active contours without edges'', IEEE TIP 2001. Malladi et al ''Shape modeling with front propagation: a level set approach.'', IEEE PAMI 1995. Nepovinné články: 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 30.9. Segmentation - shape models, Cootes et al: Active Shape Models - Their Training and Applications, Computer Vision and Image Understanding. 1995. Cootes et al: Active appearance models. IEEE PAMI 2001. 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 7.10. Segmentations - superpixels, random walker, GraphCuts, graph search, normalized cuts Achanta et al.: SLIC ''Superpixels Compared to State-of-the-Art Superpixel Methods''. IEEE PAMI 2012. Chen, Pan: ''A Survey of Graph Cuts/Graph Search Based Medical Image Segmentation''. IEEE Reviews in Biomedical Engineering. 2018 Grady: ''Random Walks for Image Segmentation'', IEEE PAMI 2006. Shi, Malik: ''Normalized Cuts and Image Segmentation,'' IEEE PAMI 2000. superpixel lecture notes, normalized cuts lecture notes random walker lecture notes
4 14.10. 11:00 Segmentation - texture, texture descriptors, textons Leung, Malik. ''Representing and recognizing the visual appearance of materials using three-dimensional textons''. International Journal of Computer Vision, 2001. M. Unser: ''Texture classification and segmentation using wavelet frames'', IEEE TIP. 1995 Reyes-Aldasoro, Bhalerao, Volumetric Texture Segmentation by Discriminant Feature Selection and Multiresolution Classification, IEEE TMI 2007. Optional: 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 ICCV1999, Nava, Kybic "Supertexton-based segmentation in early Drosophila oogenesis." , ICIP2015. Lecture notes: textons, volumetric texture, waveletdescriptors.
5 21.10. Segmentation - CNN, U-net Ronneberger et al: U-Net: ''Convolutional Networks for Biomedical Image Segmentation''. MICCAI 2015. Oktay et al: ''Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation.'' IEEE TMI 2018. Optional: 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 (741 citations) 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. Lecture notes: CNNs, U-net, Anatomically constrained CNN
28.10. holidays
6 4.11. Detection of cells and nuclei Y. Al-Kofahi et al, “''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: ''Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images''. IEEE TMI 2016 Naylor: ''Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map''. Optional: 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 11.11. Detection of vessels and fibers Frangi et al: ''Multiscale vessel enhancement filtering''. LNCS 1998 Türetken et al: ''Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming''. IEEE PAMI 2016 Türetken et al:''Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization with Geometric Prior''s. Neurinformatics. 2011 Sironi: ''Multiscale Centerline Detection'', IEEE PAMI 2016. Lecture notes: vessel detection
8 18.11. Detection of nodules and mammographic lesions 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 Setio et al: ''Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks.'' IEEE TMI 2016 Kooi et al: ''Large scale deep learning for computer aided detection of mammographic lesion.'' MIA, 2017. Optional: 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
9 25.11. Localization of organs and structures Sofka el al ''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: ''Efficient Multiple Organ Localization in CT Image Using 3D Region Proposal Network.'' IEEE TMI 2019. lecture notes
10 2.12. Registration - ICP, coherent point drift, B-splines, rigid registration, multiresolution Besl, McKay: ''A method for registration of 3D shapes''. IEEE PAMI. 1992 Myronenko, Song: Point Set Registration: ''Coherent Point Drift''. IEEE PAMI, 2010 Lecture notes. Optional: 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
11 9.12. Registration - rigid, elastic, daemons P. Thevenaz and M. Unser, ''“Optimization of mutual information for multiresolution image registration,'' IEEE TIP 2000. Jan Kybic and Michael Unser ''Fast Parametric Elastic Image Registration.'' IEEE TIP. 2003 Thirion: ''Image matching as a diffusion process: an analogy with Maxwell’s demons.'' Med. Im. Anal. 1998. lecture notes Optional: Sotiras, Paragios, Davatzikos: ''Deformable image registration: A Survey'', IEEE TMI 2013
12 16.12. Registration by CNN Balakrishnan et al: ''VoxelMorph: A Learning Framework for Deformable Medical Image Registration''. IEEE TMI 2019 X. Yang, R. Kwitt, M. Styner, and M. Niethammer, “Quicksilver: ''Fast predictive image registration-A deep learning approach,''” NeuroImage, 2017. Lecture notes Voxelmorph and Quicksilver. Optional: Sun et al. PWC-Net: ''CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume.'' CVPR 2018
13 6.1. student project presentations - to be confirmed
Deep learning (odkazy)
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 Ústřední knihovnu ČVUT.

Užitečné však mohou být i knihy, jako například tyto:

  • 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.
courses/bam33zmo/lectures/start.txt · Last modified: 2020/12/16 02:14 by kybicjan