A multiparametric magnetic resonance imaging-based virtual reality surgical navigation tool for robotic-assisted radical prostatectomy.

OBJECTIVE: Increased computational power and improved visualization hardware have generated more opportunities for virtual reality (VR) applications in healthcare. In this study, we test the feasibility of a VR-assisted surgical navigation system for robotic-assisted radical prostatectomy. MATERIAL AND METHODS: The prostate, all magnetic resonance imaging (MRI) visible tumors, and important anatomic structures like the neurovascular bundles, seminal vesicles, bladder, and rectum were contoured on a multiparametric MRI using an in-house segmentation software. Three-dimensional (3-D) VR models were rendered and evaluated in a side room of the operating room. While interacting with the VR platform, a real-time stereo video capture of the in situ prostate was obtained to render a second 3-D model. The MRI-based model was then overlaid on the real-time model by using an automated alignment algorithm. RESULTS: Ten patients were included in this study. All MRI-based VR models were examined by surgeons immediately prior to surgery and at important steps where visualization of the tumors and their proximity to surrounding anatomic structures were critical. This was mainly during the preparation of the prostatic pedicles, neurovascular plexus, the apex, and bladder neck. All participants found the system useful, especially for tumors with locally aggressive growth patterns. For small and centrally located tumors, the system was not considered beneficial due to lack of integration into the robotic console. A fully integrated system with real-time overlays within the robotic stereo viewer was found to be the ideal scenario. CONCLUSION: We deployed a preliminary VR-assisted surgical navigation tool for robotic-assisted radical prostatectomies.

Intersection To Overpass: Instance Segmentation On Filamentous Structures With An Orientation-Aware Neural Network And Terminus Pairing Algorithm.

Filamentous structures play an important role in biological systems. Extracting individual filaments is fundamental for analyzing and quantifying related biological processes. However, segmenting filamentous structures at an instance level is hampered by their complex architecture, uniform appearance, and image quality. In this paper, we introduce an orientation-aware neural network, which contains six orientation-associated branches. Each branch detects filaments with specific range of orientations, thus separating them at junctions, and turning intersections to overpasses. A terminus pairing algorithm is also proposed to regroup filaments from different branches, and achieve individual filaments extraction. We create a synthetic dataset to train our network, and annotate real full resolution microscopy images of microtubules to test our approach. Our experiments have shown that our proposed method outperforms most existing approaches for filaments extraction. We also show that our approach works on other similar structures with a road network dataset.

Semiautomated confocal imaging of fungal pathogenesis on plants: Microscopic analysis of macroscopic specimens.

The study of phenotypic variation in plant pathogenesis provides fundamental information about the nature of disease resistance. Cellular mechanisms that alter pathogenesis can be elucidated with confocal microscopy; however, systematic phenotyping platforms-from sample processing to image analysis-to investigate this do not exist. We have developed a platform for 3D phenotyping of cellular features underlying variation in disease development by fluorescence-specific resolution of host and pathogen interactions across time (4D). A confocal microscopy phenotyping platform compatible with different maize-fungal pathosystems (fungi: Setosphaeria turcica, Cochliobolus heterostrophus, and Cercospora zeae-maydis) was developed. Protocols and techniques were standardized for sample fixation, optical clearing, species-specific combinatorial fluorescence staining, multisample imaging, and image processing for investigation at the macroscale. The sample preparation methods presented here overcome challenges to fluorescence imaging such as specimen thickness and topography as well as physiological characteristics of the samples such as tissue autofluorescence and presence of cuticle. The resulting imaging techniques provide interesting qualitative and quantitative information not possible with conventional light or electron 2D imaging. Microsc. Res. Tech., 81:141-152, 2018. © 2016 Wiley Periodicals, Inc.