Automated medical image segmentation techniques
By Neeraj Sharma, L. Aggarwal. Journal of Medical Physics, 2010
896 citations recorded by the source corpus.
Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.
Subfields: Neurology
View Paper: https://www.semanticscholar.org/paper/709219b455be088a9cf90cd97cd2a75fe415b563
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In Neurology
- John T. Hancock
- Ahmed Hosny
- Hugo J. W. L. Aerts
- Sébastien Ourselin
- John Quackenbush
- Taghi M. Khoshgoftaar
- Tom Kamiel Magda Vercauteren
- Christopher R'e
Citations in the corpus
- A survey of MRI-based brain tumor segmentation methods (2014, methodology): “The semi-automatic brain tumor segmentation methods were divided into three main processes: initialization, feedback response, and evaluation [32] .”
- Understanding Deep Learning Techniques for Image Segmentation (2019, background): “d so on. 2 Motivation There have been many reviews and surveys regarding the traditional technologies associated with image segmentation [61, 160]. While some of them specialized in application areas [107, 123, 185], while other focused on specic types of algorithms [20, 19, 59]. With arrival of dee”
- Artificial intelligence in radiology (2018, positive): “Such atlases have enabled more accurate automated segmentations, as they contain information regarding the expected locations of tumours across entire patient populations 46 .”
- Inferring Generative Model Structure with Static Analysis (2017, methodology): “First, an automated segmentation algorithm is used to generate a binary mask for where the tumor is [20, 25, 34, 39].”
- Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks (2018, neutral): “Segmentation of medical images is an essential task for many applications such as anatomical structure modeling, tumor growth measurement, surgical planing and treatment assessment [1].”
- CatBoost for big data: an interdisciplinary review (2020, methodology): “Therefore, future work to apply the results of this study in a Big Data setting, would be to apply a fully automated technique for medical image segmentation, such as those covered in “Automated medical image segmentation techniques” [73], by Sharma and Aggarwal.”
- DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2017, background): “However, fully automatic segmentation methods rarely achieve sufficiently accurate and robust results to be clinically useful [2].”