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Field
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researchers across BlueMat in solving image analysis challenges, especially segmentation and quantitative extraction development of a correlative image analysis platform including image registration and
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and image annotation to support development of AI models trained on OCT scans from DME patients, including DRCR datasets. QA of segmentation of retinal layers and fluid compartments using and validating
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advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities such as CT, MRI, X-ray, and ultrasound. Research areas include image segmentation, detection
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learning-based segmentation, multimodal image fusion, and radiomic feature extraction to construct clinically relevant prognostic models. Conducted at the Heart Institute (InCor) of Hospital das Clínicas
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involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim to build more trustworthy and robust AI models that can
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, image reconstruction, and data-driven modeling of biological systems. The Division carries out research and postgraduate education within computational imaging, applied mathematics, and structural biology
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tissues and (where relevant) soil. The modelling work will be tightly coupled to experimental campaigns and will use image-derived structures (including XCT images where available) to develop mechanistic
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such as CT, MRI, X-ray, and ultrasound. Research areas include image segmentation, detection, classification, keypoint recognition, image registration, and image synthesis, with the goal of improving
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lead the image processing and computational analysis efforts, developing robust methods to register, segment, and analyse spectral micro-CT data, and — where relevant — advance reconstruction and
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, image reconstruction, and data-driven modeling of biological systems. The Division carries out research and postgraduate education within computational imaging, applied mathematics, and structural biology