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Andrea Rockall - One of the best experts on this subject based on the ideXlab platform.
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fully automatic multiorgan segmentation in normal whole body magnetic resonance imaging mri using classification forests cfs convolutional neural networks cnns and a multi atlas ma approach
Medical Physics, 2017Co-Authors: Ioannis Lavdas, Konstantinos Kamnitsas, Henrietta Mair, Amandeep Sandhu, Ben Glocker, Stuart A Taylor, Eric O. Aboagye, Daniel Rueckert, Andrea RockallAbstract:Purpose: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. Methods: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three Surface Distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average Surface Distance (ASD), root-mean-square Surface Distance (RMSSD), and Hausdorff Distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a per-organ' basis, when using different imaging combinations as input for training. Results: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 0.18, RE = 0.73 +/- 0.18, PR = 0.71 +/- 0.14, CNNs: DSC = 0.81 +/- 0.13, RE = 0.83 +/- 0.14, PR = 0.82 +/- 0.10, MA: DSC = 0.71 +/- 0.22, RE = 0.70 +/- 0.34, PR = 0.77 +/- 0.15. Mean Surface Distance metrics for all the segmented structures were: CFs: ASD = 13.5 +/- 11.3 mm, RMSSD = 34.6 +/- 37.6 mm and HD = 185.7 +/- 194.0 mm, CNNs; ASD = 5.48 +/- 4.84 mm, RMSSD = 17.0 +/- 13.3 mm and HD = 199.0 +/- 101.2 mm, MA: ASD = 4.22 +/- 2.42 mm, RMSSD = 6.13 +/- 2.55 mm, and HD = 38.9 +/- 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. Conclusions: Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance. (C) 2017 American Association of Physicists in Medicine
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fully automatic multiorgan segmentation in normal whole body magnetic resonance imaging mri using classification forests cfs convolutional neural networks cnns and a multi atlas ma approach
Medical Physics, 2017Co-Authors: Ioannis Lavdas, Konstantinos Kamnitsas, Henrietta Mair, Amandeep Sandhu, Ben Glocker, Stuart A Taylor, Eric O. Aboagye, Daniel Rueckert, Andrea RockallAbstract:PURPOSE: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. METHODS: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three Surface Distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average Surface Distance (ASD), root-mean-square Surface Distance (RMSSD), and Hausdorff Distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. RESULTS: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean Surface Distance metrics for all the segmented structures were: CFs: ASD = 13.5 ± 11.3 mm, RMSSD = 34.6 ± 37.6 mm and HD = 185.7 ± 194.0 mm, CNNs; ASD = 5.48 ± 4.84 mm, RMSSD = 17.0 ± 13.3 mm and HD = 199.0 ± 101.2 mm, MA: ASD = 4.22 ± 2.42 mm, RMSSD = 6.13 ± 2.55 mm, and HD = 38.9 ± 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. CONCLUSIONS: Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance.
Ioannis Lavdas - One of the best experts on this subject based on the ideXlab platform.
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fully automatic multiorgan segmentation in normal whole body magnetic resonance imaging mri using classification forests cfs convolutional neural networks cnns and a multi atlas ma approach
Medical Physics, 2017Co-Authors: Ioannis Lavdas, Konstantinos Kamnitsas, Henrietta Mair, Amandeep Sandhu, Ben Glocker, Stuart A Taylor, Eric O. Aboagye, Daniel Rueckert, Andrea RockallAbstract:Purpose: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. Methods: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three Surface Distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average Surface Distance (ASD), root-mean-square Surface Distance (RMSSD), and Hausdorff Distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a per-organ' basis, when using different imaging combinations as input for training. Results: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 0.18, RE = 0.73 +/- 0.18, PR = 0.71 +/- 0.14, CNNs: DSC = 0.81 +/- 0.13, RE = 0.83 +/- 0.14, PR = 0.82 +/- 0.10, MA: DSC = 0.71 +/- 0.22, RE = 0.70 +/- 0.34, PR = 0.77 +/- 0.15. Mean Surface Distance metrics for all the segmented structures were: CFs: ASD = 13.5 +/- 11.3 mm, RMSSD = 34.6 +/- 37.6 mm and HD = 185.7 +/- 194.0 mm, CNNs; ASD = 5.48 +/- 4.84 mm, RMSSD = 17.0 +/- 13.3 mm and HD = 199.0 +/- 101.2 mm, MA: ASD = 4.22 +/- 2.42 mm, RMSSD = 6.13 +/- 2.55 mm, and HD = 38.9 +/- 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. Conclusions: Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance. (C) 2017 American Association of Physicists in Medicine
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fully automatic multiorgan segmentation in normal whole body magnetic resonance imaging mri using classification forests cfs convolutional neural networks cnns and a multi atlas ma approach
Medical Physics, 2017Co-Authors: Ioannis Lavdas, Konstantinos Kamnitsas, Henrietta Mair, Amandeep Sandhu, Ben Glocker, Stuart A Taylor, Eric O. Aboagye, Daniel Rueckert, Andrea RockallAbstract:PURPOSE: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. METHODS: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three Surface Distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average Surface Distance (ASD), root-mean-square Surface Distance (RMSSD), and Hausdorff Distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. RESULTS: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean Surface Distance metrics for all the segmented structures were: CFs: ASD = 13.5 ± 11.3 mm, RMSSD = 34.6 ± 37.6 mm and HD = 185.7 ± 194.0 mm, CNNs; ASD = 5.48 ± 4.84 mm, RMSSD = 17.0 ± 13.3 mm and HD = 199.0 ± 101.2 mm, MA: ASD = 4.22 ± 2.42 mm, RMSSD = 6.13 ± 2.55 mm, and HD = 38.9 ± 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. CONCLUSIONS: Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance.
Jing Yuan - One of the best experts on this subject based on the ideXlab platform.
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longitudinal analysis of pre term neonatal cerebral ventricles from 3d ultrasound images using spatial temporal deformable registration
IEEE Transactions on Medical Imaging, 2017Co-Authors: Wu Qiu, Jessica Kishimoto, Yimin Chen, Sandrine De Ribaupierre, Aaron Fenster, Bernard Chiu, Bijoy K Menon, Jing YuanAbstract:Preterm neonates with a very low birth weight of less than 1,500 grams are at increased risk for developing intraventricular hemorrhage (IVH), which is a major cause of brain injury in preterm neonates. Quantitative measurements of ventricular dilatation or shrinkage play an important role in monitoring patients and evaluating treatment options. 3D ultrasound (US) has been developed to monitor ventricle volume as a biomarker for ventricular changes. However, ventricle volume as a global indicator does not allow for precise analysis of local ventricular changes, which could be linked to specific neurological problems often seen in the patient population later in life. In this work, a 3D+t spatial-temporal deformable registration approachis proposed, which is applied to the analysis of the detailed local changes of preterm IVH neonatal ventricles from 3D US images. In particular, a novel sequential convex/dual optimization algorithm is introduced to extract the optimal 3D+t spatial-temporal deformable field, which simultaneously optimizes the sequence of 3D deformation fieldswhile enjoying both efficiencyand simplicity in numerics. The developed registration technique was evaluated by comparing two manually extracted ventricle Surfaces from the baseline and the registered follow-up images using the metrics of Dice similarity coefficient (DSC), mean absolute Surface Distance (MAD), and maximum absolute Surface Distance (MAXD). The performed experiments using 14 patients with 5 time-point images per patient show that the proposed 3D+t registration approach accurately recovered the longitudinal deformation of ventricle Surfaces from 3D US images. The proposed approach may be potentially used to analyse the change pattern of cerebral ventricles of IVH patients, their response to different treatment options, and to elucidate the deficiencies that a patient could have later in life. To the best of our knowledge, this paper reports the first study on the longitudinalanalysis of neonatal ventricular system from 3D US images.
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automatic segmentation approach to extracting neonatal cerebral ventricles from 3d ultrasound images
Medical Image Analysis, 2017Co-Authors: Wu Qiu, Jing Yuan, Jessica Kishimoto, Yimin Chen, Sandrine De Ribaupierre, Aaron Fenster, Bernard ChiuAbstract:Preterm neonates with a very low birth weight of less than 1,500 g are at increased risk for developing intraventricular hemorrhage (IVH). Progressive ventricle dilatation of IVH patients may cause increased intracranial pressure, leading to neurological damage, such as neurodevelopmental delay and cerebral palsy. The technique of 3D ultrasound (US) imaging has been used to quantitatively monitor the ventricular volume in IVH neonates, which may elucidate the ambiguity surrounding the timing of interventions in these patients as 2D clinical US imaging relies on linear measurement and visual estimation of ventricular dilation from a series of 2D slices. To translate 3D US imaging into the clinical setting, a fully automated segmentation algorithm is necessary to extract the ventricular system from 3D neonatal brain US images. In this paper, an automatic segmentation approach is proposed to delineate lateral ventricles of preterm neonates from 3D US images. The proposed segmentation approach makes use of phase congruency map, multi-atlas initialization technique, atlas selection strategy, and a multiphase geodesic level-sets (MGLS) evolution combined with a spatial shape prior derived from multiple pre-segmented atlases. Experimental results using 30 IVH patient images show that the proposed GPU-implemented approach is accurate in terms of the Dice similarity coefficient (DSC), the mean absolute Surface Distance (MAD), and maximum absolute Surface Distance (MAXD). To the best of our knowledge, this paper reports the first study on automatic segmentation of the ventricular system of premature neonatal brains from 3D US images.
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automatic 3d us brain ventricle segmentation in pre term neonates using multi phase geodesic level sets with shape prior
Medical Image Computing and Computer-Assisted Intervention, 2015Co-Authors: Wu Qiu, Jing Yuan, Jessica Kishimoto, Yimin Chen, Martin Rajchl, Eranga Ukwatta, Sandrine De Ribaupierre, Aaron FensterAbstract:Pre-term neonates born with a low birth weight (< 1500g) are at increased risk for developing intraventricular hemorrhage (IVH). 3D ultrasound (US) imaging has been used to quantitatively monitor the ventricular volume in IVH neonates, instead of typical 2D US used clinically, which relies on linear measurements from a single slice and visually estimates to determine ventricular dilation. To translate 3D US imaging into clinical setting, an accurate segmentation algorithm would be desirable to automatically extract the ventricular system from 3D US images. In this paper, we propose an automatic multi-region segmentation approach for delineating lateral ventricles of pre-term neonates from 3D US images, which makes use of multi-phase geodesic level-sets (MP-GLS) segmentation technique via a variational region competition principle and a spatial shape prior derived from pre-segmented atlases. Experimental results using 15 IVH patient images show that the proposed GPU-implemented approach is accurate in terms of the Dice similarity coefficient (DSC), the mean absolute Surface Distance (MAD), and maximum absolute Surface Distance (MAXD). To the best of our knowledge, this paper reports the first study on automatic segmentation of ventricular system of premature neonatal brains from 3D US images.
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rotational slice based prostate segmentation using level set with shape constraint for 3d end firing trus guided biopsy
Medical Image Computing and Computer-Assisted Intervention, 2012Co-Authors: Wu Qiu, Jing Yuan, Eranga Ukwatta, David Tessier, Aaron FensterAbstract:Prostate segmentation in 3D ultrasound images is an important step in the planning and treatment of 3D end-firing transrectal ultrasound (TRUS) guided prostate biopsy. A semi-automatic prostate segmentation method is presented in this paper, which integrates a modified Distance regularization level set formulation with shape constraint to a rotational-slice-based 3D prostate segmentation method. Its performance, using different metrics, has been evaluated on a set of twenty 3D patient prostate images by comparison with expert delineations. The volume overlap ratio of 93.39±1.26% and the mean absolute Surface Distance of 1.16±0.34 mm were found in the quantitative validation result.
Ben Glocker - One of the best experts on this subject based on the ideXlab platform.
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fully automatic multiorgan segmentation in normal whole body magnetic resonance imaging mri using classification forests cfs convolutional neural networks cnns and a multi atlas ma approach
Medical Physics, 2017Co-Authors: Ioannis Lavdas, Konstantinos Kamnitsas, Henrietta Mair, Amandeep Sandhu, Ben Glocker, Stuart A Taylor, Eric O. Aboagye, Daniel Rueckert, Andrea RockallAbstract:Purpose: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. Methods: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three Surface Distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average Surface Distance (ASD), root-mean-square Surface Distance (RMSSD), and Hausdorff Distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a per-organ' basis, when using different imaging combinations as input for training. Results: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 0.18, RE = 0.73 +/- 0.18, PR = 0.71 +/- 0.14, CNNs: DSC = 0.81 +/- 0.13, RE = 0.83 +/- 0.14, PR = 0.82 +/- 0.10, MA: DSC = 0.71 +/- 0.22, RE = 0.70 +/- 0.34, PR = 0.77 +/- 0.15. Mean Surface Distance metrics for all the segmented structures were: CFs: ASD = 13.5 +/- 11.3 mm, RMSSD = 34.6 +/- 37.6 mm and HD = 185.7 +/- 194.0 mm, CNNs; ASD = 5.48 +/- 4.84 mm, RMSSD = 17.0 +/- 13.3 mm and HD = 199.0 +/- 101.2 mm, MA: ASD = 4.22 +/- 2.42 mm, RMSSD = 6.13 +/- 2.55 mm, and HD = 38.9 +/- 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. Conclusions: Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance. (C) 2017 American Association of Physicists in Medicine
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fully automatic multiorgan segmentation in normal whole body magnetic resonance imaging mri using classification forests cfs convolutional neural networks cnns and a multi atlas ma approach
Medical Physics, 2017Co-Authors: Ioannis Lavdas, Konstantinos Kamnitsas, Henrietta Mair, Amandeep Sandhu, Ben Glocker, Stuart A Taylor, Eric O. Aboagye, Daniel Rueckert, Andrea RockallAbstract:PURPOSE: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. METHODS: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three Surface Distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average Surface Distance (ASD), root-mean-square Surface Distance (RMSSD), and Hausdorff Distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. RESULTS: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean Surface Distance metrics for all the segmented structures were: CFs: ASD = 13.5 ± 11.3 mm, RMSSD = 34.6 ± 37.6 mm and HD = 185.7 ± 194.0 mm, CNNs; ASD = 5.48 ± 4.84 mm, RMSSD = 17.0 ± 13.3 mm and HD = 199.0 ± 101.2 mm, MA: ASD = 4.22 ± 2.42 mm, RMSSD = 6.13 ± 2.55 mm, and HD = 38.9 ± 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. CONCLUSIONS: Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance.
Stuart A Taylor - One of the best experts on this subject based on the ideXlab platform.
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fully automatic multiorgan segmentation in normal whole body magnetic resonance imaging mri using classification forests cfs convolutional neural networks cnns and a multi atlas ma approach
Medical Physics, 2017Co-Authors: Ioannis Lavdas, Konstantinos Kamnitsas, Henrietta Mair, Amandeep Sandhu, Ben Glocker, Stuart A Taylor, Eric O. Aboagye, Daniel Rueckert, Andrea RockallAbstract:Purpose: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. Methods: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three Surface Distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average Surface Distance (ASD), root-mean-square Surface Distance (RMSSD), and Hausdorff Distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a per-organ' basis, when using different imaging combinations as input for training. Results: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 0.18, RE = 0.73 +/- 0.18, PR = 0.71 +/- 0.14, CNNs: DSC = 0.81 +/- 0.13, RE = 0.83 +/- 0.14, PR = 0.82 +/- 0.10, MA: DSC = 0.71 +/- 0.22, RE = 0.70 +/- 0.34, PR = 0.77 +/- 0.15. Mean Surface Distance metrics for all the segmented structures were: CFs: ASD = 13.5 +/- 11.3 mm, RMSSD = 34.6 +/- 37.6 mm and HD = 185.7 +/- 194.0 mm, CNNs; ASD = 5.48 +/- 4.84 mm, RMSSD = 17.0 +/- 13.3 mm and HD = 199.0 +/- 101.2 mm, MA: ASD = 4.22 +/- 2.42 mm, RMSSD = 6.13 +/- 2.55 mm, and HD = 38.9 +/- 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. Conclusions: Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance. (C) 2017 American Association of Physicists in Medicine
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fully automatic multiorgan segmentation in normal whole body magnetic resonance imaging mri using classification forests cfs convolutional neural networks cnns and a multi atlas ma approach
Medical Physics, 2017Co-Authors: Ioannis Lavdas, Konstantinos Kamnitsas, Henrietta Mair, Amandeep Sandhu, Ben Glocker, Stuart A Taylor, Eric O. Aboagye, Daniel Rueckert, Andrea RockallAbstract:PURPOSE: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. METHODS: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three Surface Distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average Surface Distance (ASD), root-mean-square Surface Distance (RMSSD), and Hausdorff Distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. RESULTS: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean Surface Distance metrics for all the segmented structures were: CFs: ASD = 13.5 ± 11.3 mm, RMSSD = 34.6 ± 37.6 mm and HD = 185.7 ± 194.0 mm, CNNs; ASD = 5.48 ± 4.84 mm, RMSSD = 17.0 ± 13.3 mm and HD = 199.0 ± 101.2 mm, MA: ASD = 4.22 ± 2.42 mm, RMSSD = 6.13 ± 2.55 mm, and HD = 38.9 ± 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. CONCLUSIONS: Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance.
