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ResUNet++: An Advanced Architecture for Medical Image Segmentation
Accurate computer-aided polyp detection and segmentation during colonosc...
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Automated Segmentation of Vertebrae on Lateral Chest Radiography Using Deep Learning
The purpose of this study is to develop an automated algorithm for thora...
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Deep Learning-based Prediction of Key Performance Indicators for Electrical Machine
The design of an electrical machine can be quantified and evaluated by K...
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Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation
Segmentation of biomedical images can assist radiologists to make a bett...
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A superpixel-driven deep learning approach for the analysis of dermatological wounds
Background. The image-based identification of distinct tissues within de...
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Deep Mouse: An End-to-end Auto-context Refinement Framework for Brain Ventricle and Body Segmentation in Embryonic Mice Ultrasound Volumes
High-frequency ultrasound (HFU) is well suited for imaging embryonic mic...
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Real-Time Polyp Detection, Localisation and Segmentation in Colonoscopy Using Deep Learning
Computer-aided detection, localisation, and segmentation methods can hel...
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Automated Mouse Organ Segmentation: A Deep Learning Based Solution
The analysis of animal cross section images, such as cross sections of laboratory mice, is critical in assessing the effect of experimental drugs such as the biodistribution of candidate compounds in preclinical drug development stage. Tissue distribution of radiolabeled candidate therapeutic compounds can be quantified using techniques like Quantitative Whole-Body Autoradiography (QWBA).QWBA relies, among other aspects, on the accurate segmentation or identification of key organs of interest in the animal cross section image such as the brain, spine, heart, liver and others. We present a deep learning based organ segmentation solution to this problem, using which we can achieve automated organ segmentation with high precision (dice coefficient in the 0.83-0.95 range depending on organ) for the key organs of interest.
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