The increasing popularity of autonomous vehicles has heightened the need to deploy robust systems that guarantee the safety and comfort of vehicle occupants. Such systems are required to monitor the ...presence and posture of occupants in real-time. This paper proposes a novel system that combines capacitive sensing with machine learning to achieve real-time occupant detection and posture recognition. The main constituent of the system is a capacitance-sensing mat that spans both the seat base (SB) and the backrest (BR) of a typical vehicle seat. The mat is connected to a sensing circuitry that continuously monitors variations in capacitance inside the mat to generate grayscale capacitance-sensing images (CSIs). The CSIs are real-time pictorial representations of the variations in capacitance caused within the mat due to changes in the position and the posture of occupants. The real-time CSIs then serve as inputs to a k-nearest-neighbors-based (k-NN) classifier that is capable of identifying different posture classes. The classifier is pre-trained using a previously acquired database consisting of a collection of CSIs belonging to known posture classes. Subsequently, the performance of the classifier is validated, and the system is deployed for real-time occupant detection and posture recognition.
Accurate segmentation of the liver and tumors from CT volumes is crucial for hepatocellular carcinoma diagnosis and pre-operative resection planning. Despite advances in deep learning-based methods ...for abdominal CT images, fully-automated segmentation remains challenging due to class imbalance and structural variations, often requiring cascaded approaches that incur significant computational costs. In this paper, we present the Dual-Encoder Double Concatenation Network (DEDC-Net) for simultaneous segmentation of the liver and its tumors. DEDC-Net leverages both residual and skip connections to enhance feature reuse and optimize performance in liver and tumor segmentation tasks. Extensive qualitative and quantitative experiments on the LiTS dataset demonstrate that DEDC-Net outperforms existing state-of-the-art liver segmentation methods. An ablation study was conducted to evaluate different encoder backbones — specifically VGG19 and ResNet — and the impact of incorporating an attention mechanism. Our results indicate that DEDC-Net, without any additional attention gates, achieves a superior mean Dice Score (DS) of 0.898 for liver segmentation. Moreover, integrating residual connections into one encoder yielded the highest DS for tumor segmentation tasks. The robustness of our proposed network was further validated on two additional, unseen CT datasets: IDCARDb-01 and COMET. Our model demonstrated superior lesion segmentation capabilities, particularly on IRCADb-01, achieving a DS of 0.629. The code implementation is publicly available at this website.
•Novel dual-encoder architecture for automated liver and tumor segmentation.•Double concatenation enhances spatial information from low to high resolution.•VGG and ResNet encoders with a single decoder streamline network structure.•Validated on LiTS2017, 3DIRCADb, OSLO-COMET; robust and accurate without attention.
Conventional surgical navigation systems rely on preoperative imaging to provide guidance. In laparoscopic liver surgery, insufflation of the abdomen (pneumoperitoneum) can cause deformations on the ...liver, introducing inaccuracies in the correspondence between the preoperative images and the intraoperative reality. This study evaluates the improvements provided by intraoperative imaging for laparoscopic liver surgical navigation, when displayed as augmented reality (AR). Significant differences were found in terms of accuracy of the AR, in favor of intraoperative imaging. In addition, results showed an effect of user-induced error: image-to-patient registration based on annotations performed by clinicians caused 33% more inaccuracy as compared to image-to-patient registration algorithms that do not depend on user annotations. Hence, to achieve accurate surgical navigation for laparoscopic liver surgery, intraoperative imaging is recommendable to compensate for deformation. Moreover, user annotation errors may lead to inaccuracies in registration processes.
Autonomous vehicles (AV) are a promising contemporary engineering innovation. Since they have no human drivers, their vehicle controllers must be fully informed of the activities of their occupants ...in vehicle seats so that occupant safety and comfort can be guaranteed. This paper introduces a system that employs capacitive sensing and machine learning to inform the controller of occupant activities, like actions and posture changes. The system facilitates capacitive sensing by deploying a capacitance-sensing mat on the vehicle seat. A sensing circuitry connected to the mat measures all its capacitances continuously. Since an occupant's body induces variations in these capacitances, temporal sequences of capacitance measurements represent various occupant activities in the vehicle seat. Subsequently, the system converts capacitance measurements corresponding to every time step to two grayscale capacitance-sensing images (CSIs). The CSIs, in turn, yield a feature vector corresponding to every time step, thereby building a dataset of temporal sequences of features. The system's machine-learning unit tracks and recognizes occupant actions and posture changes using an action-recognition block, which is essentially a long short-term memory (LSTM) network trained on a dataset of temporal sequences of either features or capacitance measurements corresponding to various occupant actions. If the occupant remains stationary, a switching block in the unit disables the action-recognition block and enables a posture-recognition block. The latter is a k -nearest-neighbor ( k NN) classifier trained on a dataset of features to recognize stationary occupant postures. This paper validates the system's performance and investigates its deployment for real-time tracking of occupant activities in AVs.
Colorectal cancer (CRC) is the third most common type of cancer with the liver being the most common site for cancer spread. A precise understanding of patient liver anatomy and pathology, as well as ...surgical planning based on that, plays a critical role in the treatment process. In some cases, surgeons request a 3D reconstruction, which requires a thorough analysis of the available images to be converted into 3D models of relevant objects through a segmentation process. Liver vessel segmentation is challenging due to the large variations in size and directions of the vessel structures as well as difficult contrasting conditions. In recent years, deep learning-based methods had been outperforming the conventional image analysis methods in the field of medical imaging. Though Convolutional Neural Networks (CNN) have been proved to be efficient for the task of medical image segmentation, the way of handling the image data and the preprocessing techniques play an important role in segmentation. Our work focuses on the combination of different vesselness enhancement filters and preprocessing methods to enhance the hepatic vessels prior to segmentation. In the first experiment, the effect of enhancement using individual vesselness filters was studied. In the second experiment, the effect of gamma correction on vesselness filters was studied. Lastly, the effect of fused vesselness filters over individual filters was studied. The methods were evaluated on clinical CT data. The quantitative analysis of the results in terms of different evaluation metrics from experiments can be summed up as (i) each of the filtered methods shows an improvement as compared to unenhanced with the best mean DICE score of 0.800 in comparison to 0.740 for unenhanced; (ii) applied gamma correction provides a statistically significant improvement in the performance of each filter with improvement in mean DICE of around 2%; (iii) both the fused filtered images and fused segmentation give the best results (mean DICE score of 0.818 and 0.830, respectively) with the statistically significant improvement compared to the individual filters with and without Gamma correction. The results have further been verified by qualitative analysis and hence show the importance of our proposed fused filter and segmentation approaches.
Single-layer graphene (SLG) membranes have great promise as ultrahigh flux, high selectivity membranes for gas mixture separations due to their single atom thickness. It remains a central question ...whether SLG membranes of a requisite area can exist under an imposed pressure drop and temperatures needed for industrial gas separation. An additional challenge is the development of techniques to perforate or otherwise control the porosity in graphene membranes to impart molecularly sized pores, the size regime predicted to produce high gas separation factors. Herein, we report fabrication, pressure testing, temperature cycling, and gas permeance measurements through free-standing, low defect density SLG membranes. Our measurements demonstrate the remarkable chemical and mechanical stability of these 5 μm diameter suspended SLG membranes, which remain intact over weeks of testing at pressure differentials of >0.5 bar, repeated temperature cycling from 25 to 200 °C, and exposure to 15 mol % ozone for up to 3 min. These membranes act as molecularly impermeable barriers, with very low or near negligible background permeance. We also demonstrate a 1077 °C temperature O2 etching technique to create nanopores on the order of ∼1 nm diameter as imaged by scanning tunneling microscopy, although transport through such pores has not yet been successfully measured. Overall, these results represent an important advancement that will enable graphene gas separation membranes to be fabricated, tested, and modified in situ while maintaining remarkable mechanical and thermal stability.
The liver is the most frequent organ for metastasis from colorectal cancer, one of the most common tumor types with a poor prognosis. Despite reducing surgical planning time and providing better ...spatial representation, current methods of 3D modeling of patient-specific liver anatomy are extremely time-consuming. The purpose of this study was to develop a deep learning model trained on an in-house dataset of 84 MRI volumes to rapidly provide fully automated whole liver and liver lesions segmentation from volumetric MRI series. A cascade approach was utilized to address the problem of class imbalance. The trained model achieved an average Dice score for whole liver segmentation of 0.944 ± 0.009 and 0.780 ± 0.119 for liver lesion segmentation. Furthermore, applying this method to a not-annotated dataset creates a complete 3D segmentation in less than 6 s per MRI volume, with a mean segmentation Dice score of 0.994 ± 0.003 for the liver and 0.709 ± 0.171 for tumors compared to manual corrections applied after the inference was achieved. Availability and integration of our method in clinical practice may improve diagnosis and treatment planning in patients with colorectal liver metastasis and open new possibilities for research into liver tumors.
•Optical tracking with intraoperative imaging offers accurate surgical navigation.•Navigation can be updated intraoperatively using fluoroscopy.•Initial registration and updates require placement of ...fiducials around the lesion.•Local accuracy after updates is comparable to new computed tomography imaging.•Sufficient local accuracy for guidance in resections with wider margins.
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In laparoscopic liver resection, surgeons conventionally rely on anatomical landmarks detected through a laparoscope, preoperative volumetric images and laparoscopic ultrasound to compensate for the challenges of minimally invasive access. Image guidance using optical tracking and registration procedures is a promising tool, although often undermined by its inaccuracy. This study evaluates a novel surgical navigation solution that can compensate for liver deformations using an accurate and effective registration method. The proposed solution relies on a robotic C-arm to perform registration to preoperative CT/MRI image data and allows for intraoperative updates during resection using fluoroscopic images. Navigation is offered both as a 3D liver model with real-time instrument visualization, as well as an augmented reality overlay on the laparoscope camera view. Testing was conducted through a pre-clinical trial which included four porcine models. Accuracy of the navigation system was measured through two evaluation methods: liver surface fiducials reprojection and a comparison between planned and navigated resection margins. Target Registration Error with the fiducials evaluation shows that the accuracy in the vicinity of the lesion was 3.78±1.89 mm. Resection margin evaluations resulted in an overall median accuracy of 4.44 mm with a maximum error of 9.75 mm over the four subjects. The presented solution is accurate enough to be potentially clinically beneficial for surgical guidance in laparoscopic liver surgery.
Introduction: In liver surgery, medical images from pre-operative computed tomography and magnetic resonance imaging are the basis for the decision-making process. These images are used in surgery ...planning and guidance, especially for parenchyma-sparing hepatectomies. Though medical images are commonly visualized in two dimensions (2D), surgeons need to mentally reconstruct this information in three dimensions (3D) for a spatial understanding of the anatomy. The aim of this work is to investigate whether the use of a 3D model visualized in mixed reality with Microsoft HoloLens increases the spatial understanding of the liver, compared to the conventional way of using 2D images.
Material and methods: In this study, clinicians had to identify liver segments associated to lesions.
Results: Twenty-eight clinicians with varying medical experience were recruited for the study. From a total of 150 lesions, 89 were correctly assigned without significant difference between the modalities. The median time for correct identification was 23.5 4-138 s using the magnetic resonance imaging images and 6.00 1-35 s using HoloLens (p < 0.001).
Conclusions: The use of 3D liver models in mixed reality significantly decreases the time for tasks requiring a spatial understanding of the organ. This may significantly decrease operating time and improve use of resources.
•Complete workflow for use of mixed reality in surgery planning.•Conducted qualitative user evaluation from wide demographic of participants.•Conducted use-cases in planning for liver resection and ...congenital heart surgery.•Surgeons perceived the HoloLens to be useful and recommendable to others.
Meticulous preoperative planning is an important part of any surgery to achieve high levels of precision and avoid complications. Conventional medical 2D images and their corresponding three-dimensional (3D) reconstructions are the main components of an efficient planning system. However, these systems still use flat screens for visualisation of 3D information, thus losing depth information which is crucial for 3D spatial understanding. Currently, cutting-edge mixed reality systems have shown to be a worthy alternative to provide 3D information to clinicians. In this work, we describe development details of the different steps in the workflow for the clinical use of mixed reality, including results from a qualitative user evaluation and clinical use-cases in laparoscopic liver surgery and heart surgery. Our findings indicate a very high general acceptance of mixed reality devices with our applications and they were consistently rated high for device, visualisation and interaction areas in our questionnaire. Furthermore, our clinical use-cases demonstrate that the surgeons perceived the HoloLens to be useful, recommendable to other surgeons and also provided a definitive answer at a multi-disciplinary team meeting.
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