Geometric approaches have been very effective in quantifying and characterizing complex anatomical shape differences and changes in biomedical images. In image segmentation, various topological approaches such as level sets, graph cuts and fuzzy connectedness have been effective. However, it's very difficult to separate topology from geometry in images. Often the combinations of geometric and topological approaches are more effective in quantifying complex images. For instance, topological constraints are enforced to have consistent shape preserving image deformation. Theoretically, the Gauss-Bonnet theorem connects geometry and topology through a single mathematical equation. Recently, topological data analysis (TDA) has been popular in revealing topological features that are persistent over multiple scales. TDA often employs geometric methods in quantifying topological changes. The main aim of this workshop (within ISBI 2020 conference) is to increase the awareness of the interaction between geometrical and topological approaches to the ISBI community. The program will include invited talks, as well as regular oral and poster sessions with contributed research papers. Best paper and poster awards will be given.
Anuj Srivastava, Professor of Statistics and Distinguished Research Professor, Florida State University
Recent Advances in Geometric analysis of topologically-varying shapes
Jong Chul Ye KAIST Endowed Chair Professor of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST)
Geometric understanding of convolutional neural networks
Anqi Qiu, Dean’s Chair and the Deputy Head for Research & Enterprises, Associate Professor of Biomedical Engineering, National University of Singapore
Polynomial-based spectral graph convolutional neural network for diagnosis of Alzheimer’s disease
Punam Saha, Professor of Electrical & Computer Engineering and Radiology, University of Iowa.
Topological and geometric methods in osteoporotic imaging
Yuan Wang, Assistant Professor of Biostatistics, University of South Carolina.
Topological signal processing in neuroimaging studies
Yue Pan, University of Iowa
Pulmonary blood vessel and lobe surface varifold (PvSV) registration
Chao Chen, Stony Brook University
End-to-end training of neural networks with topological loss.
Rudrasis Chakraborty, University of California-Berkeley
A GMM based point-cloud generation algorithm and its application to neuroimaging
Moo Chung, University of Wisconsin-Madison
Parametric representation of sulcal and gyral trees
Xiaoyang Guo, Florida State University
Representations, metrics and statistics for shape analysis of elastic graphs
Won Hwa Kim, University of Texas-Arlington
Multi-resolution Graph Neural Network for Detecting Variations in Brain Connectivity
Arman Kulkami, University of Wisconsin-Madison
Investigating heritability across resting sate brain networks via heat kernel smoothing on persistence diagrams
Hangfan Liu, University of Pennsylvania
Cerebral Microbleed detection via Fourier descriptor with dual domain distribution modeling
Romuere Rodrigues Veloso Silva, Universidade Federal do Piauí
Fusion of color bands using genetic algorithm to segment melanoma
Tananun Songdechakraiwut, University of Wisconsin-Madison
Stationarity of Barcodes in Time Series of Brain Images
Kehong Yuan, Tsinghua University
Deformable registration using average geometric transformations for brain MR images
Kehong Yuan, Tsinghua University
GSRGAN: Medical image super-resolution using a generative adversarial network
Qing Zou, University of Iowa
Generative union of surfaces model: deep architectures re-explained
Any topic related to geometry or/and topology
Paper submission Deadline [1 page abstract]: January 29, 2020 Informally extended to sometime in February
The ISBI workshop proceedings will be archived in the IEEE Xplore Digital Library. The workshop paper format (initially 1 page abstract, optional 4 page final version) follows that of the ISBI 2020 main conference [link]. Paper should be submitted [here] with submission CODE: 67cg7
Chao Chen, Assistant Professor, Department of Biomedical Informatics, Stony Brook University
Moo K. Chung, Associate Professor, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Shantanu H. Joshi, Assistant Professor, Department of Neurology, University of California, Los Angeles
Won Hwa Kim, Assistant Professor, Department of Computer Science and Engieering, University of Texas, Arlington
Joseph Reinhardt, Professor, Biomedical Engineering, University of Iowa
Chee-Ming Ting, SeniorLecture, School of Biomedical Engineering & Health Science, Universiti Teknologi Malaysia
Yuan Wang, Assistant Professor of Biostatistics, University of South Carolina