Multi-Atlas Segmentation of Biomedical Images: A Survey

By Juan Eugenio Iglesias, Mert Rory Sabuncu. Medical Image Anal., 2014

725 citations recorded by the source corpus.

Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of “atlases” (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 – 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8d6c07158b6150b66c33543ec0cd47bd081e123a

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Citations in the corpus

MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2015, positive): “During thepast 5 years,multi-atlas segmentation (MAS)has increasingly gained attention as a potential solution to this problem (Iglesias and Sabuncu, 2015).”
HeMIS: Hetero-Modal Image Segmentation (2016, background): “These techniques have shown excellent results in structural segmentation when using non-linear registration [11]; when combined with non-local approaches they have proven effective in segmenting diﬀuse and sparse pathologies (ie. multiple sclerosis (MS) lesions [7]) as well as more complex…”
AtlasNet: Multi-atlas Non-linear Deep Networks for Medical Image Segmentation (2018, methodology): “Image segmentation is one of the most well studied problems in medical image analysis [8, 6].”
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem (2020, neutral): “They can be categorized into rule-based [8], [9], [10], [11], atlas-based [12], [13], [14], ML-based [15],”
Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, neutral): “It refers to the process of tagging image pixels or voxels with biologically meaningful labels, such as anatomical structures and tissue types [304] .”
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation (2019, background): “When multiple atlases are available, they are each aligned to a target volume, and the warped atlas labels are fused [36, 41, 68, 78].”
An application of cascaded 3D fully convolutional networks for medical image segmentation (2018, background): “So called multi-atlas label fusion (Rohlfing et al., 2004; Wang et al., 2013; Iglesias and Sabuncu, 2015) has found wide application in clinical research and practice.”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, positive): “Some of the most successful include multi-atlas segmentation [85], graph cuts [86], and active shape models [87], [88].”
Local Temperature Scaling for Probability Calibration (2020, methodology): “Finally, a label fusion method [22] G is used to produce the final estimated segmentation T̂S for TI , i.”
Understanding and Interpreting Machine Learning in Medical Image Computing Applications (2018, methodology): “One of the main obstacles that limits the usability and generalization capabilities (to new instances) of machine learning approaches is the usually small number of instances (n) of the datasets used to train the models [2].”
A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI (2016, methodology): “…current interest in methods based on the multi-atlas segmentation framework (Rohlfing et al., 2004; Klein et al., 2005; Heckemann et al., 2006), which is becoming one of the most widely used and successful image segmentation techniques in medical imaging applications (Iglesias and Sabuncu, 2015).”