Discriminative dictionary learning for abdominal multi-organ segmentation

•Discriminative dictionary learning is applied to multi-organ segmentation.•A local voxel-wise atlas selection is proposed.•A multi-resolution strategy is developed for gaining computational efficiency.•Validation is carried out on 150 abdominal CT images.•A comparison between different atlas select...

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Published in:	Medical image analysis Vol. 23; no. 1; pp. 92 - 104
Main Authors:	Tong, Tong, Wolz, Robin, Wang, Zehan, Gao, Qinquan, Misawa, Kazunari, Fujiwara, Michitaka, Mori, Kensaku, Hajnal, Joseph V., Rueckert, Daniel
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01.07.2015
Subjects:	Abdominal multi-organ segmentation Algorithms Discriminative dictionary learning Humans Kidney - diagnostic imaging Liver - diagnostic imaging Local atlas selection Pancreas - diagnostic imaging Patch based Radiographic Image Interpretation, Computer-Assisted - methods Radiography, Abdominal - methods Spleen - diagnostic imaging Tomography, X-Ray Computed - methods Local atlas selection Abdominal multi-organ segmentation Discriminative dictionary learning Patch based
ISSN:	1361-8415, 1361-8423, 1361-8423
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Abstract	•Discriminative dictionary learning is applied to multi-organ segmentation.•A local voxel-wise atlas selection is proposed.•A multi-resolution strategy is developed for gaining computational efficiency.•Validation is carried out on 150 abdominal CT images.•A comparison between different atlas selection strategies. The framework of the multi-resolution segmentation process. The proposed DDLS approach is performed to generate probabilistic atlas for each organ, which propagates across resolutions. The segmentation mask at current resolution is limited to the voxels with uncertain segmentations at the previous resolution. The final segmentation is achieved by using the graph-cuts algorithm in the native space. [Display omitted] An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.
AbstractList	An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively. An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively. •Discriminative dictionary learning is applied to multi-organ segmentation.•A local voxel-wise atlas selection is proposed.•A multi-resolution strategy is developed for gaining computational efficiency.•Validation is carried out on 150 abdominal CT images.•A comparison between different atlas selection strategies. The framework of the multi-resolution segmentation process. The proposed DDLS approach is performed to generate probabilistic atlas for each organ, which propagates across resolutions. The segmentation mask at current resolution is limited to the voxels with uncertain segmentations at the previous resolution. The final segmentation is achieved by using the graph-cuts algorithm in the native space. [Display omitted] An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.
Author	Wolz, Robin Tong, Tong Misawa, Kazunari Gao, Qinquan Fujiwara, Michitaka Wang, Zehan Hajnal, Joseph V. Rueckert, Daniel Mori, Kensaku
Author_xml	– sequence: 1 givenname: Tong surname: Tong fullname: Tong, Tong email: t.tong11@imperial.ac.uk organization: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen’s Gate, London SW7 2AZ, UK – sequence: 2 givenname: Robin surname: Wolz fullname: Wolz, Robin organization: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen’s Gate, London SW7 2AZ, UK – sequence: 3 givenname: Zehan surname: Wang fullname: Wang, Zehan organization: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen’s Gate, London SW7 2AZ, UK – sequence: 4 givenname: Qinquan surname: Gao fullname: Gao, Qinquan organization: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen’s Gate, London SW7 2AZ, UK – sequence: 5 givenname: Kazunari surname: Misawa fullname: Misawa, Kazunari organization: Aichi Cancer Center, Nagoya 464-8681, Japan – sequence: 6 givenname: Michitaka surname: Fujiwara fullname: Fujiwara, Michitaka organization: Nagoya University Hospital, Nagoya 466-0065, Japan – sequence: 7 givenname: Kensaku surname: Mori fullname: Mori, Kensaku organization: Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan – sequence: 8 givenname: Joseph V. surname: Hajnal fullname: Hajnal, Joseph V. organization: Center for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, St. Thomas Hospital, London SE1 7EH, UK – sequence: 9 givenname: Daniel surname: Rueckert fullname: Rueckert, Daniel organization: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen’s Gate, London SW7 2AZ, UK
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Keywords	Local atlas selection Abdominal multi-organ segmentation Discriminative dictionary learning Patch based
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Snippet	•Discriminative dictionary learning is applied to multi-organ segmentation.•A local voxel-wise atlas selection is proposed.•A multi-resolution strategy is... An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in...
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SubjectTerms	Abdominal multi-organ segmentation Algorithms Discriminative dictionary learning Humans Kidney - diagnostic imaging Liver - diagnostic imaging Local atlas selection Pancreas - diagnostic imaging Patch based Radiographic Image Interpretation, Computer-Assisted - methods Radiography, Abdominal - methods Spleen - diagnostic imaging Tomography, X-Ray Computed - methods
Title	Discriminative dictionary learning for abdominal multi-organ segmentation
URI	https://dx.doi.org/10.1016/j.media.2015.04.015 https://www.ncbi.nlm.nih.gov/pubmed/25988490 https://www.proquest.com/docview/1686067306
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