Quantitative Coronary Analysis
The main contribution of Quantitative Coronary Analysis (QCA) consists in measuring the diameter of arteries. Angiograms provide coronary images of region suspected of lesions using which our advanced algorithms for vessel detection and segmentation measure the segmented artery’s diameter. Abnormal values (as compared to a constructed reference diameter) are suspected as stenosis. Our system extracts and displays relevant values to the view of medical professionals and their patients.
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Right Atrium Measurement with Ultrasound
Right Atrium Measurement in Ultrasound Videos Atrial fibrillation is an irregular rhythmic beating of the heart associated with coronary heart disease, high blood pressure
Finding Cysts, Part Five: Final Detection
The goal is to automatically detect the appearance of Cystoid Macular Edema (CME) in Optical Coherence Tomography (OCT) images. The deep learning technique used, Convolutional Neural Networks, takes as an input patches of pixels from within the retina. These patches were generated from previous segmentation of retinal images. A further segmentation of the retina is performed using an image processing algorithm called SLIC. Every superpixel thus generated, after being labeled as in the OCT scan, is fed into the neural network to detect the cyst.
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Explaining OCT Scans
What are OCT Scans? Optical coherence tomography (OCT) is a non-invasive imaging method, which produces high-resolution volumetric histological images of tissue. To penetrate deep into biological
Finding Cysts Part Four: Seed Detection
A series of five articles on our Cysts Detection project using deep learning and Convolutional Neural Networks: 1) our cyst detection method; 2) the cyst denoising process; 3) the retinal layer segmentation; 4) the automatical seed-detection; 5) the final detection of the cysts. Our method is exceptionally successful at finding the cysts themselves and most of their area. Remarkable results are achieved even when using relatively small datasets in the training process.
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Finding Cysts Part Three: Layer Segmentation
A series of five articles on our Cysts Detection project using deep learning and Convolutional Neural Networks: 1) our cyst detection method; 2) the cyst denoising process; 3) the retinal layer segmentation; 4) the automatical seed-detection; 5) the final detection of the cysts. Our method is exceptionally successful at finding the cysts themselves and most of their area. Remarkable results are achieved even when using relatively small datasets in the training process.
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Finding Cysts, Part Two: The Denoising Process
A series of five articles on our Cysts Detection project using deep learning and Convolutional Neural Networks: 1) our cyst detection method; 2) the cyst denoising process; 3) the retinal layer segmentation; 4) the automatical seed-detection; 5) the final detection of the cysts. Our method is exceptionally successful at finding the cysts themselves and most of their area. Remarkable results are achieved even when using relatively small datasets in the training process.
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Automatic Detection of Macular Cysts
A series of five articles on our Cysts Detection project using deep learning and Convolutional Neural Networks: 1) our cyst detection method; 2) the cyst denoising process; 3) the retinal layer segmentation; 4) the automatical seed-detection; 5) the final detection of the cysts. Our method is exceptionally successful at finding the cysts themselves and most of their area. Remarkable results are achieved even when using relatively small datasets in the training process.
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Defining the Borders within Computer Vision
What’s the Difference between Computer Vision, Image Processing and Machine Learning? In this page, you will learn about Machine Vision, Computer Vision and Image Processing. If you
Exploring Deep Learning & CNNs
Deep Learning and Convolutional Neural Networks: RSIP Vision Blogs In this page, you will learn about Computer Vision, Machine Vision and Image Processing. If
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