Robot assisted stapedotomy ex vivo with an active handheld instrument

Micron is a fully handheld active micromanipulator that helps to improve position accuracy and precision in microsurgery by cancelling hand tremor. This work describes adaptation, tuning, and testing of the Micron system for stapedotomy, a microsurgical procedure performed in the middle ear to restore hearing that requires accurate manipulation in narrow spaces. Two end-effectors, a handle, and a brace (or rest) were designed and prototyped. The control system was adapted for the new hardware. The system was tested ex vivo in stapedotomy procedure comparing manually-performed and Micron-assisted surgical tasks. Tremor amplitude was found to be reduced significantly. Further testing is needed in order to obtain statistically significant results regarding other parameters dealing with regularity of the fenestra shap

Enhanced real-time pose estimation for closed-loop robotic manipulation of magnetically actuated capsule endoscopes

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

Computer-assisted liver graft steatosis assessment via learning-based texture analysis

Purpose: Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process. Methods: Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was 100 × 100. This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern (HLBPriu2), and gray-level co-occurrence matrix (FGLCM) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance. Results: With the best-performing feature set (HLBPriu2+INT+Blo) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively. Conclusions: This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR

Toward Improving Safety in Neurosurgery with an Active Handheld Instrument

Microsurgical procedures, such as petroclival meningioma resection, require careful surgical actions in order to remove tumor tissue, while avoiding brain and vessel damaging. Such procedures are currently performed under microscope magnification. Robotic tools are emerging in order to filter surgeons’ unintended movements and prevent tools from entering forbidden regions such as vascular structures. The present work investigates the use of a handheld robotic tool (Micron) to automate vessel avoidance in microsurgery. In particular, we focused on vessel segmentation, implementing a deep-learning-based segmentation strategy in microscopy images, and its integration with a feature-based passive 3D reconstruction algorithm to obtain accurate and robust vessel position. We then implemented a virtual-fixture-based strategy to control the handheld robotic tool and perform vessel avoidance. Clay vascular phantoms, lying on a background obtained from microscopy images recorded during petroclival meningioma surgery, were used for testing the segmentation and control algorithms. When testing the segmentation algorithm on 100 different phantom images, a median Dice similarity coefficient equal to 0.96 was achieved. A set of 25 Micron trials of 80 s in duration, each involving the interaction of Micron with a different vascular phantom, were recorded, with a safety distance equal to 2 mm, which was comparable to the median vessel diameter. Micron’s tip entered the forbidden region 24% of the time when the control algorithm was active. However, the median penetration depth was 16.9 μm, which was two orders of magnitude lower than median vessel diameter. Results suggest the system can assist surgeons in performing safe vessel avoidance during neurosurgical procedures

Smoking accelerates pancreatic cancer progression by promoting differentiation of MDSCs and inducing HB-EGF expression in macrophages

Smoking is an established risk factor for pancreatic cancer (PC), but late diagnosis limits the evaluation of its mechanistic role in the progression of PC. We used a well-established genetically engineered mouse model (LSL-K-rasG12D) of PC to elucidate the role of smoking during initiation and development of pancreatic intraepithelial neoplasia (PanIN). The 10-week-old floxed mice (K-rasG12D; Pdx-1cre) and their control unfloxed (LSL-K-rasG12D) littermates were exposed to cigarette smoke (total suspended particles: 150 mg/m3) for 20 weeks. Smoke exposure significantly accelerated the development of PanIN lesions in the floxed mice, which correlated with tenfold increase in the expression of cytokeratin19. The systemic accumulation of myeloid-derived suppressor cells (MDSCs) decreased significantly in floxed mice compared with unfloxed controls (P\u3c0.01) after the smoke exposure with the concurrent increase in the macrophage (P\u3c0.05) and dendritic cell (DCs) (P\u3c0.01) population. Further, smoking-induced inflammation (IFN-γ, CXCL2; P\u3c0.05) was accompanied by enhanced activation of pancreatic stellate cells and elevated levels of serum retinoic acid-binding protein 4, indicating increased bioavailability of retinoic acid which contributes to differentiation of MDSCs to tumor-associated macrophages (TAMs) and DCs. TAMs predominantly contribute to the increased expression of heparin-binding epidermal growth factor-like growth factor (EGFR ligand) in pre-neoplastic lesions in smoke-exposed floxed mice that facilitate acinar-to-ductal metaplasia (ADM). Further, smoke exposure also resulted in partial suppression of the immune system early during PC progression. Overall, the present study provides a novel mechanism of smoking-induced increase in ADM in the presence of constitutively active K-ras mutation

A Comparative Study of Spatio-Temporal U-Nets for Tissue Segmentation in Surgical Robotics

In surgical robotics, the ability to achieve high levels of autonomy is often limited by the complexity of the surgical scene. Autonomous interaction with soft tissues requires machines able to examine and understand the endoscopic video streams in real-time and identify the features of interest. In this work, we show the first example of spatio-temporal neural networks, based on the U-Net, aimed at segmenting soft tissues in endoscopic images. The networks, equipped with Long Short-Term Memory and Attention Gate cells, can extract the correlation between consecutive frames in an endoscopic video stream, thus enhancing the segmentation’s accuracy with respect to the standard U-Net. Initially, three configurations of the spatiotemporal layers are compared to select the best architecture. Afterwards, the parameters of the network are optimised and finally the results are compared with the standard U-Net. An accuracy of 83:77%±2:18% and a precision of 78:42%±7:38% are achieved by implementing both Long Short Term Memory (LSTM) convolutional layers and Attention Gate blocks. The results, although originated in the context of surgical tissue retraction, could benefit many autonomous tasks such as ablation, suturing and debridement

Pancreatic cancer cells resistance to gemcitabine: the role of MUC4 mucin

BACKGROUND: A major obstacle to the successful management of pancreatic cancer is to acquire resistance to the existing chemotherapeutic agents. Resistance to gemcitabine, the standard first-line chemotherapeutic agent for advanced and metastatic pancreatic cancer, is mainly attributed to an altered apoptotic threshold in the pancreatic cancer. The MUC4 transmembrane glycoprotein is aberrantly overexpressed in the pancreatic cancer and recently, has been shown to increase pancreatic tumour cell growth by the inhibition of apoptosis. METHODS: Effect of MUC4 on pancreatic cancer cells resistance to gemcitabine was studied in MUC4-expressing and MUC4-knocked down pancreatic cancer cell lines after treatment with gemcitabine by Annexin-V staining, DNA fragmentation assay, assessment of mitochondrial cytochrome c release, immunoblotting and co-immunoprecipitation techniques. RESULTS: Annexin-V staining and DNA fragmentation experiment demonstrated that MUC4 protects CD18/HPAF pancreatic cancer cells from gemcitabine-induced apoptosis. In concert with these results, MUC4 also attenuated mitochondrial cytochrome c release and the activation of caspase-9. Further, our results showed that MUC4 exerts anti-apoptotic function through HER2/extracellular signal-regulated kinase-dependent phosphorylation and inactivation of the pro-apoptotic protein Bad. CONCLUSION: Our results elucidate the function of MUC4 in imparting resistance to pancreatic cancer cells against gemcitabine through the activation of anti-apoptotic pathways and, thereby, promoting cell survival

Computer-assisted liver-graft steatosis assessment via learning-based texture analysis

Purpose: Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process. Methods: Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was (Formula presented.). This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern ((Formula presented.)), and gray-level co-occurrence matrix ((Formula presented.)) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance. Results: With the best-performing feature set ((Formula presented.)) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively. Conclusions: This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR

Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

BACKGROUND: The design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour with time, due to conditioning level and fatigue. Secondly, the easiness to use in routine clinical practice requires experienced operators. Therefore, feedback controllers, avoiding long setting procedures, are required. METHODS: The error mapping controller (EMC) here proposed uses artificial neural networks (ANNs) both for the design of an inverse model and of a feedback controller. A neuromuscular model is used to validate the performance of the controllers in simulations. The EMC performance is compared to a Proportional Integral Derivative (PID) included in an anti wind-up scheme (called PIDAW) and to a controller with an ANN as inverse model and a PID in the feedback loop (NEUROPID). In addition tests on the EMC robustness in response to variations of the Plant parameters and to mechanical disturbances are carried out. RESULTS: The EMC shows improvements with respect to the other controllers in tracking accuracy, capability to prolong exercise managing fatigue, robustness to parameter variations and resistance to mechanical disturbances. CONCLUSION: Different from the other controllers, the EMC is capable of balancing between tracking accuracy and mapping of fatigue during the exercise. In this way, it avoids overstressing muscles and allows a considerable prolongation of the movement. The collection of the training sets does not require any particular experimental setting and can be introduced in routine clinical practice