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

Abstract

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.

A Comparative Study of Prostate Cancer Detection Rate Between Transperineal COG-TB and Transperineal FUS-TB in Patients with PSA ≤20 ng/mL

“…The COG-TB technique is free from software fusion of images, and although its equipment cost is low, the operation is simple and rapid, and it can be easily popularized in clinics; the conversion of MRI images to TRUS images may lead to subjective errors. 27 Therefore, physicians should not only master the prostate ultrasound examination technology but also they should become proficient in reading MRI sequences; thus, COG-TB has a wide area under the learning curve and a long learning cycle. With the continuous simplification of the rules and regulations of the center and the continuous improvement of the biopsy process, these technologies are transferring to the outpatient setting.…”

Accurate validation of ultrasound imaging of prostate cancer: a review of challenges in registration of imaging and histopathology

“…For these purposes, respectively, recent literature features a wide range of variations of inter-modality fusion (e.g. PET–US [ 63 ], MRI–US [ 64 ], MRI–SPECT [ 65 ], and MRI–CT [ 66 ]) and intra-modality fusion techniques (e.g. US–US [ 67 ], CT–CT [ 68 ], and MRI–MRI [ 69 ]).…”

Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration

“…One application includes calculating the gland volume estimation which can be used for measuring drug-induced prostate volume changes ( Moore et al., 2017 ), for correlation with cancer volume ( Matsugasumi et al., 2015 ) and for detecting significant cancer ( Thompson et al., 2016 , Khalvati et al., 2016 ). Other applications also include for longitudinal analysis of patients undergoing active surveillance ( Stamatakis et al., 2013 ; Diaz et al., 2015 , Ouzzane et al., 2015 , Nguyen et al., 2015 , Lai et al., 2017 ), and as part of segmentation-driven multi-modal registration to support MRI-targeted transrectal-ultrasound (TRUS) guided biopsy and therapy ( Ouzzane et al., 2015 , Narayanan et al., 2009 , Onofrey et al., 2017 ). Relating the accuracy of these clinical measures to the accuracy of the output of different MRI segmentation networks (for example, those submitted to the PROMISE12 Challenge ( Fig.…”