Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement

Yin, Yin; Fotin, Sergei V.; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Cornud, F.; Türkbey, Barış; Choyke, Peter L.

doi:10.1117/12.911758

Cited by 14 publications

(7 citation statements)

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“…Chandra et al proposed a fast segmentation method based on a case specific deformable model for MR prostate scans without an endorectal coil [ 45 ]. Yin et al employed a two-step approach for fully automated and robust prostate segmentation: first, the prostate region is detected based on the cross correlation of normalized gradient fields; second, a prostate mean shape model is refined by means of a graph-search framework [ 46 ]. Deformable models are useful when noise and sampling artifacts result in invalid object boundaries.…”

Section: Computer Aided-diagnosis For Prostate Cancermentioning

confidence: 99%

Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research

Wang

Burtt

Türkbey

et al. 2014

BioMed Research International

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103

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Prostate cancer (PCa) is the most commonly diagnosed cancer among men in the United States. In this paper, we survey computer aided-diagnosis (CADx) systems that use multiparametric magnetic resonance imaging (MP-MRI) for detection and diagnosis of prostate cancer. We review and list mainstream techniques that are commonly utilized in image segmentation, registration, feature extraction, and classification. The performances of 15 state-of-the-art prostate CADx systems are compared through the area under their receiver operating characteristic curves (AUC). Challenges and potential directions to further the research of prostate CADx are discussed in this paper. Further improvements should be investigated to make prostate CADx systems useful in clinical practice.

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Section: Computer Aided-diagnosis For Prostate Cancermentioning

confidence: 99%

Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research

Wang

Burtt

Türkbey

et al. 2014

BioMed Research International

Self Cite

103

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show abstract

“…For each proposed method, we use a different strategy to extract the VOI contours from the probability map: (1) AlexNet approach: we identify the highest probability point along the normal line and use B-spline to smooth the final VOI contours. (2) HNN_mri stand-alone model: we utilize the morphology with identity object operation to filter out the largest region from the probability map and convert it to the final VOIs. (3) HNN_mri_ced multifeature stand-alone model: we directly threshold the probability map from the fifth side-output layer to generate the final VOI contours.…”

Section: Methodsmentioning

confidence: 99%

“…Shapebased models are widely used by MRIs prostate segmentation. Yin et al 2 proposed an automated segmentation model based on normalized gradient field cross-correlation for initialization and a graph-search-based framework for refinement. Ghose et al 3 proposed the use of texture features from approximation coefficients of the Haar wavelet transform for propagation of a shape and active appearance model (AAM) to segment the prostate.…”

Section: Introductionmentioning

confidence: 99%

Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks

et al. 2017

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Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity of tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. We investigate both patch-based and holistic (image-to-image) deep-learning methods for segmentation of the prostate. First, we introduce a patch-based convolutional network that aims to refine the prostate contour which provides an initialization. Second, we propose a method for end-to-end prostate segmentation by integrating holistically nested edge detection with fully convolutional networks. Holistically nested networks (HNN) automatically learn a hierarchical representation that can improve prostate boundary detection. Quantitative evaluation is performed on the MRI scans of 250 patients in fivefold cross-validation. The proposed enhanced HNN model achieves a mean ± standard deviation. A Dice similarity coefficient (DSC) of [Formula: see text] and a mean Jaccard similarity coefficient (IoU) of [Formula: see text] are used to calculate without trimming any end slices. The proposed holistic model significantly ([Formula: see text]) outperforms a patch-based AlexNet model by 9% in DSC and 13% in IoU. Overall, the method achieves state-of-the-art performance as compared with other MRI prostate segmentation methods in the literature.

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“…Klein et al 1 proposed an automatic segmentation method based on atlas matching. Yin et al 2 proposed an automated segmentation model based on normalized gradient field cross correlation and graph search-based framework. Ghose et al 3 proposed an active appearance model (AAM) to segment the prostate.…”

Section: Introductionmentioning

confidence: 99%

Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections

et al. 2019

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Accurate and automated prostate whole gland and central gland segmentations on MR images are essential for aiding any prostate cancer diagnosis system. Our work presents a 2-D orthogonal deep learning method to automatically segment the whole prostate and central gland from T2-weighted axial-only MR images. The proposed method can generate high-density 3-D surfaces from low-resolution (z axis) MR images. In the past, most methods have focused on axial images alone, e.g., 2-D based segmentation of the prostate from each 2-D slice. Those methods suffer the problems of over-segmenting or under-segmenting the prostate at apex and base, which adds a major contribution for errors. The proposed method leverages the orthogonal context to effectively reduce the apex and base segmentation ambiguities. It also overcomes jittering or stair-step surface artifacts when constructing a 3-D surface from 2-D segmentation or direct 3-D segmentation approaches, such as 3-D U-Net. The experimental results demonstrate that the proposed method achieves 92.4% AE 3% Dice similarity coefficient (DSC) for prostate and DSC of 90.1% AE 4.6% for central gland without trimming any ending contours at apex and base. The experiments illustrate the feasibility and robustness of the 2-D-based holistically nested networks with short connections method for MR prostate and central gland segmentation. The proposed method achieves segmentation results on par with the current literature.

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Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement

Cited by 14 publications

References 0 publications

Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research

Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research

Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks

Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections

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