Johan Öfverstedt
Forskare vid Institutionen för informationsteknologi; Vi3; Bildanalys
- E-post:
- johan.ofverstedt@it.uu.se
- Besöksadress:
- Hus 10, Lägerhyddsvägen 1
- Postadress:
- Box 337
751 05 UPPSALA
Postdoktor vid Institutionen för kirurgiska vetenskaper; Övriga; Radiologisk bildanalys
- E-post:
- johan.ofverstedt@uu.se
- Besöksadress:
- Dag Hammarskjölds v 14 B Floor 2
75237 Uppsala - Postadress:
- Dag Hammarskjölds v 14 B Floor 2
75237 Uppsala
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Kort presentation
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I am a postdoctoral researcher in the PET/MR research group headed by Professor Joel Kullberg and Professor Håkan Ahlström where I am researching topics related to medical image registration and deep image regression.
My PhD research was in method development for efficient fusion of intensity and spatial information, distance/similarity measures between sets/images, image registration, and machine learning methods.
Nyckelord
- deep learning
- distance transforms
- image analysis
- image registration
- machine learning
- optimization
- robustness
- similarity measures
Forskning
Denna text finns inte på svenska, därför visas den engelska versionen.
Reviewed publications
2021
L Solorzano, L. Wik, T. O. Bontell, Y. Wang, A. H. Klemm, J. Öfverstedt, A. S. Jakola, A. Östman, C. Wählby: Machine learning for cell classification and neighborhood analysis in glioma tissue. Cytometry Part A, 2021.
2020
N. Pielawski, E. Wetzer, J. Öfverstedt, J. Lu, C. Wählby, J. Lindblad, and N. Sladoje. CoMIR: Contrastive Multimodal Image Representations for Registration. NeurIPS 2020.
J. Öfverstedt, J. Lindblad, and N. Sladoje. Stochastic Distance Transform: Theory, Algorithms, and Applications. Journal of Mathematical Imaging and Vision, 62(5), 751-769, 2020. (Online - Open Access/CC BY)
2019
J. Öfverstedt, J. Lindblad, and N. Sladoje. Stochastic Distance Transform. (Preprint - arXiv:1810.08097 [cs.CV]). In Proceedings of the 21th international conference on Discrete Geometry for Computer Imagery (DGCI), Lecture Notes in Computer Science, LNCS-11134, pp. 75--86, Paris, France, March 2019. (Online).
J. Öfverstedt, J. Lindblad, and N. Sladoje. Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information. IEEE Transactions on Image Processing, Vol. 27, No. 7, pp. 3584-3597, 2019. (Online - Open Access/CC BY) (Preprint - arXiv:1807.11599 [cs.CV]).
2017
J. Öfverstedt, N. Sladoje, and J. Lindblad. Distance Between Vector-valued Fuzzy Sets based on Intersection Decomposition with Applications in Object Detection. In Proc. of the 13th International Symposium on Mathematical Morphology, ISMM2017, Fontainebleau, France, Lecture Notes in Computer Science, LNCS-10225, pp. 395-407, Springer 2017.
Publikationer
Urval av publikationer
- INSPIRE (2023)
- Cross-Sim-NGF: FFT-Based Global Rigid Multimodal Alignment of Image Volumes using Normalized Gradient Fields (2022)
- CoMIR: Contrastive Multimodal Image Representation for Registration (2020)
- Stochastic Distance Transform (2020)
- Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information (2019)
Senaste publikationer
- Multimodal deformable image registration using contrastive learning of equivariant image representations (2023)
- Contrastive Learning of Equivariant Image Representations for Multimodal Deformable Registration (2023)
- INSPIRE (2023)
- Is image-to-image translation the panacea for multimodal image registration? (2022)
- Rotationally Equivariant Representation Learning for Multimodal Images (2022)
Alla publikationer
Artiklar
- INSPIRE (2023)
- Is image-to-image translation the panacea for multimodal image registration? (2022)
- Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment (2022)
- Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study (2021)
- Machine learning for cell classification and neighborhood analysis in glioma tissue (2021)
- Cross-Sim-NGF: FFT-Based Global Rigid Multimodal Alignment of Image Volumes using Normalized Gradient Fields (2021)
- Stochastic Distance Transform (2020)
- Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information (2019)
- Global Parameter Optimization for Multimodal Biomedical Image Registration
Böcker
Konferenser
- Multimodal deformable image registration using contrastive learning of equivariant image representations (2023)
- Contrastive Learning of Equivariant Image Representations for Multimodal Deformable Registration (2023)
- Rotationally Equivariant Representation Learning for Multimodal Images (2022)
- Efficient Algorithms for Global Multimodal Image Registration (2022)
- Cross-Sim-NGF: FFT-Based Global Rigid Multimodal Alignment of Image Volumes using Normalized Gradient Fields (2022)
- Image-to-Image Translation in Multimodal Image Registration: How Well Does It Work? (2021)
- Contrastive Learning for Equivariant Multimodal Image Representations (2021)
- Registration of Multimodal Microscopy Images using CoMIR – learned structural image representations (2021)
- Comir: Contrastive multimodal image representation for registration (2021)
- Fast Computation of Mutual Information with Application to Global Multimodal Image Alignment of Micrographs (2021)
- CoMIR: Contrastive Multimodal Image Representation for Registration (2020)
- Stochastic Distance Transform (2019)
- Distance between vector-valued fuzzy sets based on intersection decomposition with applications in object detection (2017)
