Abstract-Background: Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semiautomatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast).Objective: This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches.Method: This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted.Discussion: Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. Conclusion:No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.

Abstract-Robotic surgery is transforming the current surgical practice, not only by improving the conventional surgical methods but also by introducing innovative robot-enhanced approaches that broaden the capabilities of clinicians. Being mainly of manmachine collaborative type, surgical robots are seen as media that transfer pre-and intra-operative information to the operator and reproduce his/her motion, with appropriate filtering, scaling, or limitation, to physically interact with the patient. The field, however, is far from maturity and, more critically, is still a subject of controversy in medical communities. Limited or absent haptic feedback is reputed to be among reasons that impede further spread of surgical robots. In this paper objectives and challenges of deploying haptic technologies in surgical robotics is discussed and a systematic review is performed on works that have studied the effects of providing haptic information to the users in major branches of robotic surgery. It has been tried to encompass both classical works and the state of the art approaches, aiming at delivering a comprehensive and balanced survey both for researchers starting their work in this field and for the experts.

FreeSurfer software package automatically estimates the cerebral cortical thickness. Its use is widely accepted, albeit this tool was validated against histologic measurements in only two post-mortem isolated brain MR scans. Indeed, a comparison between histologic measurements and FreeSurfer estimation from in vivo data was never performed. At the "Claudio Munari" Center for Epilepsy and Parkinson Surgery we have included FreeSurfer in our presurgical workflow since 2008, mainly because the automatic reconstruction of the brain surface is useful for carefully planning the surgical resection. We therefore compared cortical thickness values obtained by the automatic software pipeline with manual histologic measurements performed on 27 histologic specimens resected from the corresponding brain regions of the same epileptic subjects. This method-comparison study, including Passing-Bablok regression and Bland-Altman plot analysis, showed a good agreement between FreeSurfer estimation and histologic measurements of cortical thickness. The mean cortical thickness values (±Standard Deviation) obtained with FreeSurfer and histologic measurements were 3.65 mm ± 0.44 and 3.72 mm ± 0.36, respectively (P value = 0.32). Our findings strengthen previous reports on cortical thickness changes as biomarkers of different neurological conditions.

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In this paper, an improved recurrent neural network (RNN) scheme is proposed to perform the trajectory control of redundant robot manipulators using remote center of motion (RCM) constraints. Firstly, learning by demonstration is implemented to model the surgical operation skills in the Cartesian space. After that, considering the kinematic constraints associated with the optimization control of redundant manipulators, we propose a novel RNN-based approach to facilitate accurate task tracking based on the general quadratic performance index, which includes managing the constraints on RCM joint angle, and joint velocity, simultaneously. The results of the conducted theoretical analysis confirm that the RCM constraint has been established successfully, and accordingly. The corresponding end-effector tracking errors asymptotically converge to zero. Finally, demonstration experiments are conducted in a laboratory setup environment using KUKA LWR4+ to validate the effectiveness of the proposed control strategy.

Pose estimation methods for robotically guided magnetic actuation of capsule endoscopes have recently enabled trajectory following and automation of repetitive endoscopic maneuvers. However, these methods face significant challenges in their path to clinical adoption including the presence of regions of magnetic field singularity, where the accuracy of the system degrades, and the need for accurate initialization of the capsule's pose. In particular, the singularity problem exists for any pose estimation method that utilizes a single source of magnetic field if the method does not rely on the motion of the magnet to obtain multiple measurements from different vantage points. We analyze the workspace of such pose estimation methods with the use of the point-dipole magnetic field model and show that singular regions exist in areas where the capsule is nominally located during magnetic actuation. Since the dipole model can approximate most magnetic field sources, the problem discussed herein pertains to a wider set of pose estimation techniques. We then propose a novel hybrid approach employing static and time-varying magnetic field sources and show that this system has no regions of singularity. The proposed system was experimentally validated for accuracy, workspace size, update rate and performance in regions of magnetic singularity. The system performed as well or better than prior pose estimation methods without requiring accurate initialization and was robust to magnetic singularity. Experimental demonstration of closed-loop control of a tethered magnetic device utilizing the developed pose estimation technique is provided to ascertain its suitability for robotically guided capsule endoscopy. Hence, advances in closed-loop control and intelligent automation of magnetically actuated capsule endoscopes can be further pursued toward clinical realization by employing this pose estimation system.

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Early stage diagnosis of laryngeal squamous cell carcinoma (SCC) is of primary importance for lowering patient mortality or after treatment morbidity. Despite the challenges in diagnosis reported in the clinical literature, few efforts have been invested in computer-assisted diagnosis. The objective of this paper is to investigate the use of texture-based machine-learning algorithms for early stage cancerous laryngeal tissue classification. To estimate the classification reliability, a measure of confidence is also exploited. From the endoscopic videos of 33 patients affected by SCC, a well-balanced dataset of 1320 patches, relative to four laryngeal tissue classes, was extracted. With the best performing feature, the achieved median classification recall was 93% [interquartile range [Formula: see text]]. When excluding low-confidence patches, the achieved median recall was increased to 98% ([Formula: see text]), proving the high reliability of the proposed approach. This research represents an important advancement in the state-of-the-art computer-assisted laryngeal diagnosis, and the results are a promising step toward a helpful endoscope-integrated processing system to support early stage diagnosis.