Segmentation of rectum from CT images using K-means clustering for the EBRT of prostate cancer

Kalyani, Chenigaram; Swamy, M. S. Mallikarjuna

doi:10.1109/iceeccot.2016.7955181

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Clustering‐based methods extract a set of feature vectors with the goal of identifying groups or clusters of similar objects based on the feature vectors. Proximity measures are used to group data into clusters of similar types Machine learning‐based methods regard prostate as a learning‐based target .…”

Section: Introductionmentioning

confidence: 99%

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CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

et al. 2019

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Purpose Accurate segmentation of the prostate on computed tomography (CT) for treatment planning is challenging due to CT's poor soft tissue contrast. Magnetic resonance imaging (MRI) has been used to aid prostate delineation, but its final accuracy is limited by MRI‐CT registration errors. We developed a deep attention‐based segmentation strategy on CT‐based synthetic MRI (sMRI) to deal with the CT prostate delineation challenge without MRI acquisition. Methods and materials We developed a prostate segmentation strategy which employs an sMRI‐aided deep attention network to accurately segment the prostate on CT. Our method consists of three major steps. First, a cycle generative adversarial network was used to estimate an sMRI from CT images. Second, a deep attention fully convolution network was trained based on sMRI and the prostate contours deformed from MRIs. Attention models were introduced to pay more attention to prostate boundary. The prostate contour for a query patient was obtained by feeding the patient's CT images into the trained sMRI generation model and segmentation model. Results The segmentation technique was validated with a clinical study of 49 patients by leave‐one‐out experiments and validated with an additional 50 patients by hold‐out test. The Dice similarity coefficient, Hausdorff distance, and mean surface distance indices between our segmented and deformed MRI‐defined prostate manual contours were 0.92 ± 0.09, 4.38 ± 4.66, and 0.62 ± 0.89 mm, respectively, with leave‐one‐out experiments, and were 0.91 ± 0.07, 4.57 ± 3.03, and 0.62 ± 0.65 mm, respectively, with hold‐out test. Conclusions We have proposed a novel CT‐only prostate segmentation strategy using CT‐based sMRI, and validated its accuracy against the prostate contours that were manually drawn on MRI images and deformed to CT images. This technique could provide accurate prostate volume for treatment planning without requiring MRI acquisition, greatly facilitating the routine clinical workflow.

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Section: Introductionmentioning

confidence: 99%

“…Proximity measures are used to group data into clusters of similar types. 13 4. Machine learning-based methods regard prostate as a learning-based target.…”

Section: Introductionmentioning

confidence: 99%

CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

et al. 2019

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“…The proposed method is nearly correct, easy and efficient. The bounding box representation of ROI will decrease the processing time for volume rendering and hence proves to be efficient over the conventional method for utilizing the whole image [12] Abba Suganda Girsang(2016) et al presents About the algorithm to maintain the best fitness value, this paper presents a model robust algorithm for solving the problem of clustering, namely Robust Adaptive Genetic KMeans, Algorithm, called RAGKA has been shown in this paper. At first solving the problems of clustering using K-Means, but K-Means are often trapped local optimum, then K-Means combined with GA.…”

Section: K-nearest Neighbour (K-nn)mentioning

confidence: 99%

Hybrid Technique Based on Clustering for Crime Detection in Data Mining

2017

IJAERD

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Segmentation of rectum from CT images using K-means clustering for the EBRT of prostate cancer

Cited by 2 publications

References 7 publications

CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

Hybrid Technique Based on Clustering for Crime Detection in Data Mining

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