Statistical Biomechanical Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions

Khallaghi, Siavash; Sánchez, C. Antonio; Rasoulian, Abtin; Nouranian, Saman; Romagnoli, Cesare; Abdi, Hamidreza; Chang, Silvia D.; Black, Peter C.; Goldenberg, Larry; Morris, William; Spadinger, Ingrid; Fenster, Aaron; Ward, Aaron D.; Fels, Sidney; Abolmaesumi, Purang

doi:10.1109/tmi.2015.2443978

Cited by 24 publications

(30 citation statements)

References 43 publications

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“…Moradi et al (Moradi et al, 2012) non-rigidly register manually segmented prostate binary image masks, while Fedorov et al (Fedorov et al, 2015) non-rigidly register signed distance maps of manually segmented prostates. Surface-based non-rigid registration methods treat the segmentations as points in 2D slice contours (Reynier et al, 2004; Cool et al, 2011; Mitra et al, 2012a; Zettinig et al, 2015), as points in 3D surfaces (Narayanan et al, 2009; Karnik et al, 2010; Natarajan et al, 2011; Fedorov et al, 2015; Khallaghi et al, 2015b,a; van de Ven et al, 2015; Onofrey et al, 2015b), or as a set of basis functions, for example as spherical harmonics (Moradi et al, 2012). The state-of-the-art, clinical Artemis prostate biopsy system and its ProFuse Bx multimodal image fusion software (Eigen, Grass Valley, CA), relies on intra-procedure, semi-automated TRUS segmentation (Ladak et al, 2000) and a deformable surface registration algorithm to align the prostate surfaces segmented in both the MRI and TRUS images (Narayanan et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Surface-based registration algorithms such as robust point matching (RPM) (Rangarajan et al, 1997; Chui and Rangarajan, 2003) and coherent point drift (CPD) (Myronenko and Song, 2010) attempt to handle noisy surface data by allowing for soft point correspondences and outliers in the data. MR-TRUS fusion methods have made use of RPM (Khallaghi et al, 2015b; Onofrey et al, 2015b) and CPD (Fedorov et al, 2015; Khallaghi et al, 2015b,a; Zettinig et al, 2015). These methods have been shown to be robust to noisy segmentations with synthetically added perturbations (Onofrey et al, 2015b; Khallaghi et al, 2015a) and to partial segmentations missing large sections of the prostate (Fedorov et al, 2015; Khallaghi et al, 2015b,a).…”

Section: Introductionmentioning

confidence: 99%

“…A general criticism of surface-based registration methods is that they can only reliably register data that is close the surface itself since there is no information provided to the registration algorithm inside the shape boundary far from the surface. Biomechanical models address this limitation by acting as a regularization constraint on the non-rigid transformation (Moradi et al, 2012; Hu et al, 2012, 2015; Fedorov et al, 2015; Khallaghi et al, 2015b,a; van de Ven et al, 2015). These models treat the prostate as a linear elastic material, and require tuned parameters to best approximate the elastic tissue properties of the gland.…”

Section: Introductionmentioning

confidence: 99%

“…SSMs and SDMs have been shown to be promising for MR-TRUS fusion (Yan et al, 2010; Hu et al, 2012, 2015; Khallaghi et al, 2015a). In particular, Hu et al (Hu et al, 2012, 2015) train a patient-specific deformation model using simulated finite element mesh deformations generated from manual segmentation of the prostate and surrounding anatomy, while Khallaghi et al (Khallaghi et al, 2015a) use an SSM to estimate prostate shapes given segmentations with missing data.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, Hu et al (Hu et al, 2012, 2015) train a patient-specific deformation model using simulated finite element mesh deformations generated from manual segmentation of the prostate and surrounding anatomy, while Khallaghi et al (Khallaghi et al, 2015a) use an SSM to estimate prostate shapes given segmentations with missing data. In general, PCA-based models suffer from the curse of dimensionality where the number of training samples is limited compared to the higher dimensionality of the shape or the deformation field.…”

Section: Introductionmentioning

confidence: 99%

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Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention

Onofrey¹,

Staib²,

Sarkar

et al. 2017

Medical Image Analysis

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Accurate and robust non-rigid registration of pre-procedure magnetic resonance (MR) imaging to intra-procedure trans-rectal ultrasound (TRUS) is critical for image-guided biopsies of prostate cancer. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. TRUS-guided biopsy is the current clinical standard for prostate cancer diagnosis and assessment. State-of-the-art, clinical MR-TRUS image fusion relies upon semi-automated segmentations of the prostate in both the MR and the TRUS images to perform non-rigid surface-based registration of the gland. Segmentation of the prostate in TRUS imaging is itself a challenging task and prone to high variability. These segmentation errors can lead to poor registration and subsequently poor localization of biopsy targets, which may result in false-negative cancer detection. In this paper, we present a non-rigid surface registration approach to MR-TRUS fusion based on a statistical deformation model (SDM) of intra-procedural deformations derived from clinical training data. Synthetic validation experiments quantifying registration volume of interest overlaps of the PI-RADS parcellation standard and tests using clinical landmark data demonstrate that our use of an SDM for registration, with median target registration error of 2.98 mm, is significantly more accurate than the current clinical method. Furthermore, we show that the low-dimensional SDM registration results are robust to segmentation errors that are not uncommon in clinical TRUS data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention

Onofrey¹,

Staib²,

Sarkar

et al. 2017

Medical Image Analysis

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Deformable MR‐CBCT prostate registration using biomechanically constrained deep learning networks

Wang

Lei

et al. 2020

Medical Physics

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Background and purpose Radiotherapeutic dose escalation to dominant intraprostatic lesions (DIL) in prostate cancer could potentially improve tumor control. The purpose of this study was to develop a method to accurately register multiparametric magnetic resonance imaging (MRI) with CBCT images for improved DIL delineation, treatment planning, and dose monitoring in prostate radiotherapy. Methods and materials We proposed a novel registration framework which considers biomechanical constraint when deforming the MR to CBCT. The registration framework consists of two segmentation convolutional neural networks (CNN) for MR and CBCT prostate segmentation, and a three‐dimensional (3D) point cloud (PC) matching network. Image intensity‐based rigid registration was first performed to initialize the alignment between MR and CBCT prostate. The aligned prostates were then meshed into tetrahedron elements to generate volumetric PC representation of the prostate shapes. The 3D PC matching network was developed to predict a PC motion vector field which can deform the MRI prostate PC to match the CBCT prostate PC. To regularize the network’s motion prediction with biomechanical constraints, finite element (FE) modeling‐generated motion fields were used to train the network. MRI and CBCT images of 50 patients with intraprostatic fiducial markers were used in this study. Registration results were evaluated using three metrics including dice similarity coefficient (DSC), mean surface distance (MSD), and target registration error (TRE). In addition to spatial registration accuracy, Jacobian determinant and strain tensors were calculated to assess the physical fidelity of the deformation field. Results The mean and standard deviation of our method were 0.93 ± 0.01, 1.66 ± 0.10 mm, and 2.68 ± 1.91 mm for DSC, MSD, and TRE, respectively. The mean TRE of the proposed method was reduced by 29.1%, 14.3%, and 11.6% as compared to image intensity‐based rigid registration, coherent point drifting (CPD) nonrigid surface registration, and modality‐independent neighborhood descriptor (MIND) registration, respectively. Conclusion We developed a new framework to accurately register the prostate on MRI to CBCT images for external beam radiotherapy. The proposed method could be used to aid DIL delineation on CBCT, treatment planning, dose escalation to DIL, and dose monitoring.

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A novel registration‐based algorithm for prostate segmentation via the combination of SSM and CNN

Qin

Chen

et al. 2022

Medical Physics

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Precise determination of target is an essential procedure in prostate interventions, such as prostate biopsy, lesion detection, and targeted therapy. However, the prostate delineation may be tough in some cases due to tissue ambiguity or lack of partial anatomical boundary. In this study, we proposed a novel supervised registrationbased algorithm for precise prostate segmentation, which combine the convolutional neural network (CNN) with a statistical shape model (SSM). Methods: The proposed network mainly consists of two branches. One called SSM-Net branch was exploited to predict the shape transform matrix, shape control parameters, and shape fine-tuning vector, for the generation of the prostate boundary. Furtherly, according to the inferred boundary, a normalized distance map was calculated as the output of SSM-Net. Another branch named ResU-Net was employed to predict a probability label map from the input images at the same time. Integrating the output of these two branches, the optimal weighted sum of the distance map and the probability map was regarded as the prostate segmentation. Results: Two public datasets PROMISE12 and NCI-ISBI 2013 were utilized to evaluate the performance of the proposed algorithm. The results demonstrate that the segmentation algorithm achieved the best performance with an SSM of 9500 nodes, which obtained a dice of 0.907 and an average surface distance of 1.85 mm. Compared with other methods, our algorithm delineates the prostate region more accurately and efficiently. In addition, we verified the impact of model elasticity augmentation and the fine-tuning item on the network segmentation capability. As a result, both factors have improved the delineation accuracy, with dice increased by 10% and 7% respectively. Conclusions: Our segmentation method has the potential to be an effective and robust approach for prostate segmentation.

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Statistical Biomechanical Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions

Cited by 24 publications

References 43 publications

Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention

Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention

Deformable MR‐CBCT prostate registration using biomechanically constrained deep learning networks

A novel registration‐based algorithm for prostate segmentation via the combination of SSM and CNN

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