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Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels [PDF]
, 2022 Recent work has shown that label-efficient few-shot learning through self-supervision can achieve promising medical image segmentation results. However, few-shot segmentation models typically rely on prototype representations of the semantic classes ...
Jenssen, Robert +3 more
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BMC Medical Imaging, 2021 Background The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical ...
Dominik Müller, Frank Kramer
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Semi-supervised Learning for Real-time Segmentation of Ultrasound Video Objects: A Review [PDF]
Advanced Ultrasound in Diagnosis and Therapy, 2023 Real-time intelligent segmentation of ultrasound video object is a demanding task in the field of medical image processing and serves as an essential and critical step in image-guided clinical procedures.
Jin Guo, MD, Zhaojun Li, PhD, Yanping Lin, PhD
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Object segmentation by fitting statistical shape models : a Kernel-based approach with application to wisdom tooth segmentation from CBCT images [PDF]
, 2014 Image segmentation is an important and challenging task in medical image analysis. Especially from low-quality images, segmentation algorithms have to cope with misleading background clutter, insufficient object boundaries and noise in the image ...
Jud, Christoph
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DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling
NeuroImageThis study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational ...
Zongren Li +3 more
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Frontiers in Public Health, 2022 To avoid the problems of relative overlap and low signal-to-noise ratio (SNR) of segmented three-dimensional (3D) multimodal medical images, which limit the effect of medical image diagnosis, a 3D multimodal medical image segmentation algorithm using ...
Weiwei Gao +3 more
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Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation [PDF]
, 2011 Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying ...
Alzubi, S +5 more
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Image segmentation, evaluation, and applications [PDF]
, 2010 This thesis aims to advance research in image segmentation by developing robust techniques for evaluating image segmentation algorithms. The key contributions of this work are as follows. First, we investigate the characteristics of existing measures for
McGuinness, Kevin
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Current Directions in Biomedical Engineering, 2017 Multimodal imaging is gaining in importance in the field of personalized medicine and can be described as a current trend in medical imaging diagnostics. The segmentation, classification and analysis of tissue structures is essential.
Stich Manuel +3 more
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Application of Image Segmentation Technology Based on Machine Learning in Medical Image Analysis [PDF]
ITM Web of ConferencesMedical image analysis heavily relies on the crucial step of image segmentation, which possesses the capability to discern and differentiate various structures within medical imagery.
Zhang Yurun
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