EP-1893: Automatic contouring of soft organs for image-guided prostate radiotherapy

Cai, Xiaojun; Schönlieb, Carola‐Bibiane; Lee, J.; Scaife, J.E.; Karl, Hans Josef; Sutcliffe, M.P.F.; Parker, M. A.; Burnet, N.G.

doi:10.1016/s0167-8140(16)33144-9

Cited by 4 publications

(3 citation statements)

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“…Another important use for the manual contour data set was to define search boundaries to provide a starting point for the automated algorithm (Scaife et al, 2015, Cai et al, 2016). The automated contouring algorithm itself is based on the method developed by Chan and Vese in 2001 for segmenting non-medical images (Chan and Vese, 2001).…”

Section: Methods and Datamentioning

confidence: 99%

“…At the level of the prostate itself, the imaging cannot distinguish the anterior border of the rectum from the posterior edge of the prostate. However, since these 2 structures are anatomically fixed, we use the anterior part of the contour from the planning scan to provide this border (Cai et al, 2016, Sutcliffe et al, 2015, Scaife, 2016).…”

Section: Methods and Datamentioning

confidence: 99%

See 1 more Smart Citation

Applying physical science techniques and CERN technology to an unsolved problem in radiation treatment for cancer: the multidisciplinary ‘VoxTox’ research programme

Burnet

Scaife

Romanchikova

et al. 2017

CERN IdeaSquare Journal of Experimental Innovation

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Section: Methods and Datamentioning

confidence: 99%

Section: Methods and Datamentioning

confidence: 99%

Applying physical science techniques and CERN technology to an unsolved problem in radiation treatment for cancer: the multidisciplinary ‘VoxTox’ research programme

Burnet

Scaife

Romanchikova

et al. 2017

CERN IdeaSquare Journal of Experimental Innovation

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“…[26,35,11,39,26,48,49,58,5] and references therein. Moreover, various applications were put forward for instance in optical flow [19], tomographic imaging [3], and medical imaging [59,14,15,12,51,20].…”

mentioning

confidence: 99%

A Two-stage Classification Method for High-dimensional Data and Point Clouds

Cai,

Chan,

Xie

et al. 2019

Preprint

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High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose a two-stage multiphase semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a fuzzy classification method such as the standard support vector machine is used to generate a warm initialization. We then apply a two-stage approach named SaT (smoothing and thresholding) to improve the classification. In the first stage, an unconstraint convex variational model is implemented to purify and smooth the initialization, followed by the second stage which is to project the smoothed partition obtained at stage one to a binary partition. These two stages can be repeated, with the latest result as a new initialization, to keep improving the classification quality. We show that the convex model of the smoothing stage has a unique solution and can be solved by a specifically designed primal-dual algorithm whose convergence is guaranteed. We test our method and compare it with the state-of-the-art methods on several benchmark data sets. The experimental results demonstrate clearly that our method is superior in both the classification accuracy and computation speed for high-dimensional data and point clouds.

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An Efficient and Versatile Variational Method for High-Dimensional Data Classification

Cai,

Chan,

Xie

et al. 2024

J Sci Comput

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High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose an efficient and versatile multi-class semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a warm initialization is generated by using a fuzzy classification method such as the standard support vector machine or random labeling. Then an unconstraint convex variational model is proposed to purify and smooth the initialization, followed by a step which is to project the smoothed partition obtained previously to a binary partition. These steps can be repeated, with the latest result as a new initialization, to keep improving the classification quality. We show that the convex model of the smoothing step has a unique solution and can be solved by a specifically designed primal–dual algorithm whose convergence is guaranteed. We test our method and compare it with the state-of-the-art methods on several benchmark data sets. Thorough experimental results demonstrate that our method is superior in both the classification accuracy and computation speed for high-dimensional data and point clouds.

show abstract

EP-1893: Automatic contouring of soft organs for image-guided prostate radiotherapy

Cited by 4 publications

References 0 publications

Applying physical science techniques and CERN technology to an unsolved problem in radiation treatment for cancer: the multidisciplinary ‘VoxTox’ research programme

Applying physical science techniques and CERN technology to an unsolved problem in radiation treatment for cancer: the multidisciplinary ‘VoxTox’ research programme

A Two-stage Classification Method for High-dimensional Data and Point Clouds

An Efficient and Versatile Variational Method for High-Dimensional Data Classification

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