Shape-based Interpolation Using Modified Cubic Splines

Herman, Gabor T.; Bucholtz, C.A.; Zheng, Jason

doi:10.1109/iembs.1991.683941

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…This algorithm also requires an extensive manual configuration; therefore, it is not universal by default. Albu et al 10 5 as demonstrated, outperformed MBI, modified cubic spline method, 12 and CSGI. Combining the binary weighted averaging for contour-based interpolation and random forest for intensity-based classification, another automatic method improved the accuracy of the interpolation more than morphology.…”

Section: Introductionmentioning

confidence: 91%

“…improved the MBI method in 2011 by adding image intensity-based classification. The new method, called morphology-based interpolation with local intensity information (MBILII), 5 as demonstrated, outperformed MBI, modified cubic spline method, 12 and CSGI. Combining the binary weighted averaging for contour-based interpolation and random forest for intensity-based classification, another automatic method improved the accuracy of the interpolation more than morphology 13 .…”

Section: Introductionmentioning

confidence: 93%

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Contour interpolation by deep learning approach

2022

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.PurposeContour interpolation is an important tool for expediting manual segmentation of anatomical structures. The process allows users to manually contour on discontinuous slices and then automatically fill in the gaps, therefore saving time and efforts. The most used conventional shape-based interpolation (SBI) algorithm, which operates on shape information, often performs suboptimally near the superior and inferior borders of organs and for the gastrointestinal structures. In this study, we present a generic deep learning solution to improve the robustness and accuracy for contour interpolation, especially for these historically difficult cases.ApproachA generic deep contour interpolation model was developed and trained using 16,796 publicly available cases from 5 different data libraries, covering 15 organs. The network inputs were a 128 × 128 × 5 image patch and the two-dimensional contour masks for the top and bottom slices of the patch. The outputs were the organ masks for the three middle slices. The performance was evaluated on both dice scores and distance-to-agreement (DTA) values.ResultsThe deep contour interpolation model achieved a dice score of 0.95 ± 0.05 and a mean DTA value of 1.09 ± 2.30 mm, averaged on 3167 testing cases of all 15 organs. In a comparison, the results by the conventional SBI method were 0.94 ± 0.08 and 1.50 ± 3.63 mm, respectively. For the difficult cases, the dice score and DTA value were 0.91 ± 0.09 and 1.68 ± 2.28 mm by the deep interpolator, compared with 0.86 ± 0.13 and 3.43 ± 5.89 mm by SBI. The t-test results confirmed that the performance improvements were statistically significant (p < 0.05) for all cases in dice scores and for small organs and difficult cases in DTA values. Ablation studies were also performed.ConclusionsA deep learning method was developed to enhance the process of contour interpolation. It could be useful for expediting the tasks of manual segmentation of organs and structures in the medical images.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 93%

Contour interpolation by deep learning approach

2022

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“…Для него нельзя задать одинаковое количество краевых условий c обоих концов, как для полиномов третьей и пятой степени, но возможно дозадать одно срединное условие: точку, производную или вторую производную [5,[8][9][10][11][12][13][14][15][16]. Это дает дополнительные возможности применения сплайна в интерполяции и аппроксимации.…”

Section: анализ литературных данныхunclassified

Research of algorithm for calculating the vector-parametric bispline based on polynomial of the fourth degree

Ковтун

2016

TAPR

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“…Кривые линии и поверхности могут служить для создания тел. Исследования сплайнов высших степеней с управляющими точками, принадлежащими кривой [4][5][6][7][8][9][10][11][12][13][14], показали возможность получения бисплайна на основе таких линий с соответствующими свойствами. То есть получения векторно-параметрической поверхности с управляющими точками, инцидентными поверхности, с достижением гладкости первого, второго и третьего порядков.…”

Section: анализ литературных данных и постановка проблемыunclassified

Exploration of describing the vector-parametric bi-spline, defined by the forth degree spline with control points incident with surface of appropriate smoothness

Ковтун¹

2015

TAPR

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Shape-based Interpolation Using Modified Cubic Splines

Cited by 7 publications

References 10 publications

Contour interpolation by deep learning approach

Contour interpolation by deep learning approach

Research of algorithm for calculating the vector-parametric bispline based on polynomial of the fourth degree

Exploration of describing the vector-parametric bi-spline, defined by the forth degree spline with control points incident with surface of appropriate smoothness

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