Accurate Prostate Volume Estimation Using Multifeature Active Shape Models on T2-weighted MRI

Tóth, Róbert; Bloch, B; Genega, Elizabeth M.; Rofsky, Neil M.; Lenkinski, Robert E.; Rosen, Mark; Kalyanpur, Arjun; Pungavkar, Sona; Madabhushi, Anant

doi:10.1016/j.acra.2011.01.016

Cited by 48 publications

(37 citation statements)

References 26 publications

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“…is uncertain ( 32 ), it remains a choice whether to prefer a method that aligns better with pathologic or in vivo planimetry fi ndings. The correlation between results speaks to the overall precision of volume estimation methods.…”

Section: Genitourinary Imaging: Computer-derived Prostate Volumes Fromentioning

confidence: 99%

“…The trained statistical shape model is then used to constrain and smooth the segmentation to only valid prostate shapes; thus, the prostate is segmented. This method was previously used for successful volume estimations of the prostate by Toth et al ( 32 ), where the MFA-derived volumes were compared with manually estimated volumes obtained by planimetric estimation of the prostate capsule derived from manual segmentations by an expert radiologist on T2-weighted MR images.…”

Section: Pv Estimates With the Ellipsoid Formulamentioning

confidence: 99%

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Automated Computer-derived Prostate Volumes from MR Imaging Data: Comparison with Radiologist-derived MR Imaging and Pathologic Specimen Volumes

Bulman¹,

Tóth²,

Patel

et al. 2012

Radiology

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Purpose:To compare prostate gland volume (PV) estimation of automated computer-generated multifeature active shape models (MFAs) performed with 3-T magnetic resonance (MR) imaging with that of other methods of PV assessment, with pathologic specimens as the reference standard. Materials and Methods:All subjects provided written informed consent for this HIPAA-compliant and institutional review board-approved study. Freshly weighed prostatectomy specimens from 91 patients (mean age, 59 years; range, 42-84 years) served as the reference standard. PVs were manually calculated by two independent readers from MR images by using the standard ellipsoid formula. Planimetry PV was calculated from gland areas generated by two independent investigators by using manually drawn regions of interest.Computer-automated assessment of PV with an MFA was determined by the aggregate computer-calculated prostate area over the range of axial T2-weighted prostate MR images. Linear regression, linear mixed-effects models, concordance correlation coeffi cients, and Bland-Altman limits of agreement were used to compare volume estimation methods. Results:MFA-derived PVs had the best correlation with pathologic specimen PVs (slope, 0.888). Planimetry derived volumes produced slopes of 0.864 and 0.804 for two independent readers when compared with specimen PVs. Ellipsoid formula-derived PVs had slopes closest to one when compared with planimetry PVs. Manual MR imaging and MFA PV estimates had high concordance correlation coefficients with pathologic specimens. Conclusion:MFAs with axial T2-weighted MR imaging provided an automated and effi cient tool with which to assess PV. Both MFAs and MR imaging planimetry require adjustments for optimized PV accuracy when compared with prostatectomy specimens.q RSNA, 2012

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Section: Genitourinary Imaging: Computer-derived Prostate Volumes Fromentioning

confidence: 99%

Section: Pv Estimates With the Ellipsoid Formulamentioning

confidence: 99%

Automated Computer-derived Prostate Volumes from MR Imaging Data: Comparison with Radiologist-derived MR Imaging and Pathologic Specimen Volumes

Bulman¹,

Tóth²,

Patel

et al. 2012

Radiology

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“…Recently, most studies in T2-weighted MR prostate segmentation focus on two types of methods: multi-atlas-based [4][5][6][7] and deformable-model-based [8,9] segmentation methods. Multi-atlas-based methods are widely used in medical imaging [10][11][12].…”

Section: A Related Workmentioning

confidence: 99%

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

Guo

Gao

Shen

2017

Deep Learning for Medical Image Analysis

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Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods.Correspondence to: Dinggang Shen, dgshen@med.unc.edu. † Co-first authors. HHS Public Access

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“…Magnetic resonance imaging (MRI) has higher accuracy in the detection of prostate cancer and is considered to be the most effective imaging techniques at present [1]. Prostate segmentation from MRI is a necessary first step and plays a key role in different stages of clinical decision making process [2][3][4][5][6][7]. However, manual segmentation of a prostate is a laborious and time consuming work and different physician is easy to make the different segmentation results.…”

Section: Introductionmentioning

confidence: 99%

MR T1 Image Segmentation of a Prostate Based on Distance Regularized Level Set Evolution

Zhang¹,

Peng²,

Liu³

et al. 2016

IJHIT

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Accurate Prostate Volume Estimation Using Multifeature Active Shape Models on T2-weighted MRI

Cited by 48 publications

References 26 publications

Automated Computer-derived Prostate Volumes from MR Imaging Data: Comparison with Radiologist-derived MR Imaging and Pathologic Specimen Volumes

Automated Computer-derived Prostate Volumes from MR Imaging Data: Comparison with Radiologist-derived MR Imaging and Pathologic Specimen Volumes

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

MR T1 Image Segmentation of a Prostate Based on Distance Regularized Level Set Evolution

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