Publications by authors named "G Samei"

Purpose: The purpose of the study was to assess the feasibility of performing intraoperative dosimetry for permanent prostate brachytherapy by combining transrectal ultrasound (TRUS) and fluoroscopy/cone beam CT [CBCT] images and accounting for the effect of prostate deformation.

Methods And Materials: 13 patients underwent TRUS and multiview two-dimensional fluoroscopic imaging partway through the implant, as well as repeat fluoroscopic imaging with the TRUS probe inserted and retracted, and finally three-dimensional CBCT imaging at the end of the implant. The locations of all the implanted seeds were obtained from the fluoroscopy/CBCT images and were registered to prostate contours delineated on the TRUS images based on a common subset of seeds identified on both image sets.

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We propose an image guidance system for robot assisted laparoscopic radical prostatectomy (RALRP). A virtual 3D reconstruction of the surgery scene is displayed underneath the endoscope's feed on the surgeon's console. This scene consists of an annotated preoperative Magnetic Resonance Image (MRI) registered to intraoperative 3D Trans-rectal Ultrasound (TRUS) as well as real-time sagittal 2D TRUS images of the prostate, 3D models of the prostate, the surgical instrument and the TRUS transducer.

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Purpose: Prostate cancer is the most prevalent form of male-specific cancers. Robot-assisted laparoscopic radical prostatectomy (RALRP) using the da Vinci surgical robot has become the gold-standard treatment for organ-confined prostate cancer. To improve intraoperative visualization of anatomical structures, many groups have developed techniques integrating transrectal ultrasound (TRUS) into the surgical workflow.

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We present a novel technique for real-time deformable registration of 3-D to 2.5-D transrectal ultrasound (TRUS) images for image-guided, robot-assisted laparoscopic radical prostatectomy (RALRP). For RALRP, a pre-operatively acquired 3-D TRUS image is registered to thin-volumes comprised of consecutive intra-operative 2-D TRUS images, where the optimal transformation is found using a gradient descent method based on analytical first and second order derivatives.

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Purpose: Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences between the two domains, such as the smaller degree of variability in the shape and appearance of the target volume and the smaller amounts of training data in medical applications. We propose a CNN-based method for prostate segmentation in MRI that employs statistical shape models to address these issues.

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