1. Sandro De Zanet

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    1. Mentioned In 4 Articles

    2. Comparison of Drusen Volume Assessed by Two Different OCT Devices

      Comparison of Drusen Volume Assessed by Two Different OCT Devices
      To compare drusen volume between Heidelberg Spectral Domain (SD-) and Zeiss Swept-Source (SS) PlexElite Optical Coherence Tomography (OCT) determined by manual and automated segmentation methods. Thirty-two eyes of 24 patients with Age-Related Macular Degeneration (AMD) and drusen maculopathy were included. In the central 1 and 3 mm ETDRS circle drusen volumes were calculated and compared. Drusen segmentation was performed using automated manufacturer algorithms of the two OCT devices. Then, the ...
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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. Classification of Drusen Positions in Optical Coherence Tomography Data from Patients with Age-Related Macular Degeneration

      Classification of Drusen Positions in Optical Coherence Tomography Data from Patients with Age-Related Macular Degeneration
      Quantitative analysis of optical coherence tomography volumes is an important tool for both clinicians and researchers. Until now, most work has focused on segmentation of the intraretinal cell layers, but the segmentation of pathological datasets remains challenging. We propose the application of random forest to detect the locations of drusen in the retinal pigment epithelium. This is an important step for further analysis of optical coherence tomography data, for segmentation ...
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    5. 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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  2. About Sandro De Zanet

    Sandro De Zanet is a PhD student at the ARTORG Center for Biomedical Engineering Research at the University Bern, Switzerland. He received his BS and MS degrees in Computer Science from the University of Bern. His current research includes image-based 3D reconstruction and structured light.