| Seznam |
| Téma: | Segmentace vláken v papíru z mikro CT dat |
|---|---|
| Katedra: | Katedra kybernetiky |
| Vedoucí: | prof. Dr. Ing. Jan Kybic |
| Vypsáno jako: | Diplomová práce, Bakalářská práce, Semestrální projekt |
| Popis: | The aim of the project is to create a method for automatic
segmentation of paper fibers from micro CT images. Instructions 1. Read the data and manual segmentations and visualize them. 2. Get acquainted with existing methods based on provided references. 3. Get acquainted with the PyTorch library, try some simple image segmentation task using the UNet architecture. 4. Predict the fiber location in 2D using deep learning. I recommend to formulate the task as a regression problem on the transformed distance map (perhaps the Gaussian of the distance to the nearest fiber center), similar to Naylor et al. or Mertanová. 5. Connect the fiber centers using a suitable algorithm (minimum path methods, particle filtering, iterative global methods) 6. Evaluate the segmentation quantitatively on the paper fibers as well as on other fiber datasets. 7. Consider existing deep learning approaches (e.g. DeepTract, Learn to Track, Turetken et al. ) and suggest how to apply them on our data. |
| Literatura: | Thomas Dietenbeck, François Varray, Jan Kybic, Olivier Basset and Christian Cachard. "Neuromuscular fiber segmentation through particle filtering and discrete optimization." SPIE Medical Imaging, vol. 9034, pp. 90340B, February 2014.
ftp://cmp.felk.cvut.cz/pub/cmp/articles/kybic/Dietenbeck-SPIEMI2014.pdf Learn to Track: Deep Learning for Tractography https://www.biorxiv.org/content/10.1101/146688v1 Benou, Raviv: DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography. 2018 https://www.researchgate.net/publication/329641599_DeepTract_A_Probabilistic_Deep_Learning_Framework_for_White_Matter_Fiber_Tractography Naylor: Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map. IEEE TMI 2019. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8438559&tag=1 Mertanová: "Cell segmentation in microscopy using a reference modality". CTU diploma thesis, 2021 ftp://cmp.felk.cvut.cz/pub/cmp/articles/kybic/MertanovaMs2021.pdf Turetken et al: Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming. IEEE PAMI 2016 https://infoscience.epfl.ch/record/201670/files/turetken16_1.pdf DOI : 10.1109/Tpami.2016.2519025. |