1. Jens H. Kowal

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    1. Mentioned In 7 Articles

    2. Time-Resolved Ultra–High Resolution Optical Coherence Tomography for Real-Time Monitoring of Selective Retina Therapy

      Time-Resolved Ultra–High Resolution Optical Coherence Tomography for Real-Time Monitoring of Selective Retina Therapy
      Purpose : Selective retina therapy (SRT) is a novel treatment for retinal pathologies, solely targeting the RPE. During SRT, the detection of an immediate tissue reaction is challenging, as tissue effects remain limited to intracellular RPE photodisruption. Time-resolved ultra-high axial resolution optical coherence tomography (OCT) is thus evaluated for the monitoring of dynamic optical changes at and around the RPE during SRT. Methods : An experimental OCT system with an ultra-high axial ...
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    3. Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model

      Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model
      Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal ...
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    4. Noninvasive referencing of intraocular tumors for external beam radiation therapy using optical coherence tomography: A proof of concept

      Noninvasive referencing of intraocular tumors for external beam radiation therapy using optical coherence tomography: A proof of concept
      ...5 Michael B. Rüegsegger^1, Dominik Geiser^2, Patrick Steiner^3, Alessia Pica^4, Daniel M. Aebersold^4 and Jens H. Kowal^5,a) Scitation Author Page PubMed Google Scholar + View Affiliations - Hide Affiliations Affilia...
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    5. Feature Of The Week 11/8/12: University Of Bern Presents Methods for Multi-Surface Segmentation of OCT Images

      Feature Of The Week 11/8/12: University Of Bern Presents Methods for Multi-Surface Segmentation of OCT Images
      Over the past several years and for the foreseeable future, development and implementation of algorithms for processing of OCT images has become a fertile area for researchers at universities and product development engineers at OCT system companies. Such algorithms can dramatically improve OCT images and extract new information, both of which can improve clinical decision making and add tremendous value and differentiation to a product line. Topical areas include removing ...
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    6. Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

      Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints
      Optical Coherence Tomography is a well established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graphbased multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a ...
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    7. Pathology Hinting as the Combination of Automatic Segmentation with a Statistical Shape Model

      Pathology Hinting as the Combination of Automatic Segmentation with a Statistical Shape Model
      With improvements in acquisition speed and quality, the amount of medical image data to be screened by clinicians is starting to become challenging in the daily clinical practice. To quickly visualize and find abnormalities in medical images, we propose a new method combining segmentation algorithms with statistical shape models. A statistical shape model built from a healthy population will have a close fit in healthy regions. The model will however ...
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  2. About Jens H. Kowal

    Jens H. Kowal

    Jens H. Kowal received the M.S. degree in Technical Computer Science from the Technical University Berlin (Germany) in 1997. In 2002 he received his Ph.D. in Biomedical Engineering from the University of Bern (Switzerland). He worked as a postdoctoral research fellow for the Tufts University and MASS Ear Eye Infirmary in Boston (USA), the M.E. Müller Institute in Bern (Switzerland) and the University of Western Australia in Perth (Australia). From 2004 until 2008 he was heading a group for “Smart Surgical Instrumentation” at the ISTB Research Center for Surgical Technology and Biomechanics at the University of Bern (Switzerland). In 2009 he became an Assistant Professor for Ophthalmic Technologies at the ARTORG Center for Biomedical Engineering Research at the University of Bern (Switzerland).