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 |