International journal of computer assisted radiology and surgery
-
Int J Comput Assist Radiol Surg · Jan 2020
Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks.
Early detection and treatment of lung cancer holds great importance. However, pulmonary-nodule classification using CT images alone is difficult to realize. To address this concern, a method for pulmonary-nodule classification based on a deep convolutional neural network (DCNN) and generative adversarial networks (GAN) has previously been proposed by the authors. In that method, the said classification was performed exclusively using axial cross sections of pulmonary nodules. During actual medical-examination procedures, however, a comprehensive judgment can only be made via observation of various pulmonary-nodule cross sections. In the present study, a comprehensive analysis was performed by extending the application of the previously proposed DCNN- and GAN-based automatic classification method to multiple cross sections of pulmonary nodules. ⋯ This study reports development of a comprehensive analysis method to classify pulmonary nodules at multiple sections using GAN and DCNN. The effectiveness of the proposed discrimination method based on use of multiplanar images has been demonstrated to be improved compared to that realized in a previous study reported by the authors. In addition, the possibility of enhancing classification accuracy via application of GAN-generated images, instead of data augmentation, for pretraining even for medical datasets that contain relatively few images has also been demonstrated.
-
Int J Comput Assist Radiol Surg · Nov 2019
Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume.
Hydrocephalus is a clinically significant condition which can have devastating consequences if left untreated. Currently available methods for quantifying this condition using CT imaging are unreliable and prone to error. The purpose of this study is to investigate the clinical utility of using convolutional neural networks to calculate ventricular volume and explore limitations. ⋯ Two-dimensional convolutional neural network architectures can be used to accurately segment and quantify intracranial ventricle volume. While further refinements are necessary, it is likely these networks could be used as a clinical tool to quantify hydrocephalus accurately and efficiently.
-
Int J Comput Assist Radiol Surg · Oct 2019
ReviewToward versatile cooperative surgical robotics: a review and future challenges.
Surgical robotics has developed throughout the past 30 years resulting in more than 5000 different approaches proposed for various surgical disciplines supporting different surgical task sequences and differing ways of human-machine cooperation or degrees of automation. However, this diversity of systems influences cost as well as usability and might hinder their widespread adoption. In combination with the current trend toward open and modular "plug and play" dynamic networks of medical devices and IT systems in the operating room, a modular human-robot system design with versatile access to cooperative functions with varying degrees of automation on demand is desirable. Therefore, standardized robotic device profiles describing essential functional characteristics of cooperative robotic systems are mandatory. ⋯ Modular system design can be expanded toward functionalities or different degrees of autonomy, shared or manual control. The proposed device profiles of cooperative surgical robots could lay the foundation for integration into open and modular dynamic "plug and play" networks in the operating room to enhance versatility, benefit-to-cost ratio and, thereby, market spread of surgical robotics.
-
Int J Comput Assist Radiol Surg · Oct 2019
Segmentation-based registration of ultrasound volumes for glioma resection in image-guided neurosurgery.
In image-guided surgery for glioma removal, neurosurgeons usually plan the resection on images acquired before surgery and use them for guidance during the subsequent intervention. However, after the surgical procedure has begun, the preplanning images become unreliable due to the brain shift phenomenon, caused by modifications of anatomical structures and imprecisions in the neuronavigation system. To obtain an updated view of the resection cavity, a solution is to collect intraoperative data, which can be additionally acquired at different stages of the procedure in order to provide a better understanding of the resection. A spatial mapping between structures identified in subsequent acquisitions would be beneficial. We propose here a fully automated segmentation-based registration method to register ultrasound (US) volumes acquired at multiple stages of neurosurgery. ⋯ The segmented structures demonstrated to be good candidates to register US volumes acquired at different neurosurgical phases. Therefore, our solution can compensate brain shift in neurosurgical procedures involving intraoperative US data.
-
Int J Comput Assist Radiol Surg · Oct 2019
Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH)-phase II: rupture risk assessment.
Assessing the rupture probability of intracranial aneurysms (IAs) remains challenging. Therefore, hemodynamic simulations are increasingly applied toward supporting physicians during treatment planning. However, due to several assumptions, the clinical acceptance of these methods remains limited. ⋯ MATCH compares state-of-the-art image-based blood flow simulation approaches to assess the rupture risk of IAs. Furthermore, this challenge highlights the importance of multivariate analyses by combining clinically relevant metadata with advanced morphological and hemodynamic quantification.