Multiple template deformation application to abdominal organ segmentation

Gauriau, Romane; Ardori, Roberto; Lesage, David; Bloch, Isabelle

doi:10.1109/isbi.2015.7163887

Cited by 12 publications

(5 citation statements)

References 6 publications

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“…A precise three-dimensional segmentation can achieve an accurate volumetric measurement of the spleen. Methods such as multiatlas (12)(13)(14), graph-cut (13)(14)(15), active shape models (16), active contours (17), level-sets (18), and random forest (19) have been extensively used to segment the spleen.…”

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confidence: 99%

Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning

Mamani

Bukala

Scholten

et al. 2020

Radiology: Artificial Intelligence

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To develop a fully automated algorithm for spleen segmentation and to assess the performance of this algorithm in a large dataset. Materials and Methods:In this retrospective study, a three-dimensional deep learning network was developed to segment the spleen on thorax-abdomen CT scans. Scans were extracted from patients undergoing oncologic treatment from 2014 to 2017. A total of 1100 scans from 1100 patients were used in this study, and 400 were selected for development of the algorithm. For testing, a dataset of 50 scans was annotated to assess the segmentation accuracy and was compared against the splenic index equation. In a qualitative observer experiment, an enriched set of 100 scan-pairs was used to evaluate whether the algorithm could aid a radiologist in assessing splenic volume change. The reference standard was set by the consensus of two other independent radiologists. A Mann-Whitney U test was conducted to test whether there was a performance difference between the algorithm and the independent observer. Results:The algorithm and the independent observer obtained comparable Dice scores (P = .834) on the test set of 50 scans of 0.962 and 0.964, respectively. The radiologist had an agreement with the reference standard in 81% (81 of 100) of the cases after a visual classification of volume change, which increased to 92% (92 of 100) when aided by the algorithm. Conclusion:A segmentation method based on deep learning can accurately segment the spleen on CT scans and may help radiologists to detect abnormal splenic volumes and splenic volume changes.

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confidence: 99%

Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning

Mamani

Bukala

Scholten

et al. 2020

Radiology: Artificial Intelligence

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“…Moreover its fuzziness property may be useful in many types of clinical applications, such as segmentation (for initialization) or visualization (to target the structures of interest for 3D rendering) for instance. It has been recently successfully applied in an automatic segmentation framework (Gauriau et al, 2015). The confidence maps are used for both the template initialization and the computation of organ-dedicated image forces.…”

Section: Resultsmentioning

confidence: 99%

Multi-organ localization with cascaded global-to-local regression and shape prior

Gauriau

Cuingnet

Lesage

et al. 2015

Medical Image Analysis

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“…Furthermore, the semi-automatic approaches of Belgherbi and Bessaid (2012) and Soroushmehr et al (2015) require user interaction and cannot be compared to our fully automatized framework concept. Some approaches apply their algorithms in contrast-enhanced CT images (Linguraru et al 2010, Wolz et al 2013, Gauriau et al 2015, but CT imaging is not applicable in our epidemiological study due to ethical reasons Lee et al (2012) and Farraher et al (2005) apply semi-automatic methods that require a higher level of user interaction. Furthermore, in Lee et al (2012) diffusion-weighted MR images are used that show distinctive spleen borders due to the high contrast between the spleen and adjacent organs.…”

Section: Discussionmentioning

confidence: 99%

“…A manual method is necessary to train landmarks on manually segmented 2D-organ contours that are combined in a 3D landmark representation. Gauriau et al (2015) present a multiple template deformation framework to segment liver, heart, gallbladder, kidneys and spleen in a single approach. They use regression random forests with shape priors to localize the target organs in CT volume datasets.…”

Section: Related Workmentioning

confidence: 99%

Automatized spleen segmentation in non-contrast-enhanced MR volume data using subject-specific shape priors

et al. 2017

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To develop the first fully automated 3D spleen segmentation framework derived from T1-weighted magnetic resonance (MR) imaging data and to verify its performance for spleen delineation and volumetry. This approach considers the issue of low contrast between spleen and adjacent tissue in non-contrast-enhanced MR images. Native T1-weighted MR volume data was performed on a 1.5 T MR system in an epidemiological study. We analyzed random subsamples of MR examinations without pathologies to develop and verify the spleen segmentation framework. The framework is modularized to include different kinds of prior knowledge into the segmentation pipeline. Classification by support vector machines differentiates between five different shape types in computed foreground probability maps and recognizes characteristic spleen regions in axial slices of MR volume data. A spleen-shape space generated by training produces subject-specific prior shape knowledge that is then incorporated into a final 3D level set segmentation method. Individually adapted shape-driven forces as well as image-driven forces resulting from refined foreground probability maps steer the level set successfully to the segment the spleen. The framework achieves promising segmentation results with mean Dice coefficients of nearly 0.91 and low volumetric mean errors of 6.3%. The presented spleen segmentation approach can delineate spleen tissue in native MR volume data. Several kinds of prior shape knowledge including subject-specific 3D prior shape knowledge can be used to guide segmentation processes achieving promising results.

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Multiple template deformation application to abdominal organ segmentation

Cited by 12 publications

References 6 publications

Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning

Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning

Multi-organ localization with cascaded global-to-local regression and shape prior

Automatized spleen segmentation in non-contrast-enhanced MR volume data using subject-specific shape priors

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