2,093 research outputs found

    SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization

    Full text link
    Clinical research on smart healthcare has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. To this end, we have developed SenseCare research platform for smart healthcare, which is designed to boost translational research on intelligent diagnosis and treatment planning in various clinical scenarios. To facilitate clinical research with Artificial Intelligence (AI), SenseCare provides a range of AI toolkits for different tasks, including image segmentation, registration, lesion and landmark detection from various image modalities ranging from radiology to pathology. In addition, SenseCare is clinic-oriented and supports a wide range of clinical applications such as diagnosis and surgical planning for lung cancer, pelvic tumor, coronary artery disease, etc. SenseCare provides several appealing functions and features such as advanced 3D visualization, concurrent and efficient web-based access, fast data synchronization and high data security, multi-center deployment, support for collaborative research, etc. In this paper, we will present an overview of SenseCare as an efficient platform providing comprehensive toolkits and high extensibility for intelligent image analysis and clinical research in different application scenarios.Comment: 11 pages, 10 figure

    Artificial intelligence in cancer imaging: Clinical challenges and applications

    Get PDF
    Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

    Multi-Modality Breast MRI Segmentation Using nn-UNet for Preoperative Planning of Robotic Surgery Navigation

    Get PDF
    Segmentation of the chest region and breast tissues is essential for surgery planning and navigation. This paper proposes the foundation for preoperative segmentation based on two cascaded architectures of deep neural networks (DNN) based on the state-of-the-art nnU-Net. Additionally, this study introduces a polyvinyl alcohol cryogel (PVA-C) breast phantom based on the segmentation of the DNN automated approach, enabling the experiments of navigation system for robotic breast surgery. Multi-modality breast MRI datasets of T2W and STIR images were acquired from 10 patients. Segmentation evaluation utilized the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. First, a single class labeling was used to segment the breast region. Then it was employed as an input for three-class labeling to segment fat, fibroglandular (FGT) tissues, and tumorous lesions. The first architecture has a 0.95 DCS, while the second has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively

    Imaging Evaluation of Liver Tumors in Pediatric Patients

    Get PDF
    Imaging plays crucial roles in the management of pediatric patients with suspected liver malignant tumors. Three-dimensional (3D) imaging could significantly improve the resection rate of pediatric tumors and increase the safety of the surgery. With the development of medical imaging, 3D reconstruction technology, the innovation of liver surgery and the proposal of precise hepatectomy, the intrahepatic vascular anatomy of the liver and liver segmentectomy based on that vascular anatomy have become well developed. With the analysis of 3D digital liver, we proposed a new type of liver classification system: Dong’s digital liver classification system. And we measured the normal total liver volume from neonate to aging making a reference for surgeons all around the world. And the Human Digital Liver Database was established by the Affiliated Hospital of Qingdao University and Hisense Company, aiming to collect digital liver from neonates, children, adults, and the elderly, from normal livers, livers with cancer, and simulated livers resected using Hisense CAS. Then we showed one case report of patient with giant liver tumor. With the application of Hisense CAS and our data, we successfully removed the tumor. We believe that the new techniques in imaging will help surgeons to accomplish better operations

    Optimization of computer-assisted intraoperative guidance for complex oncological procedures

    Get PDF
    MenciĂłn Internacional en el tĂ­tulo de doctorThe role of technology inside the operating room is constantly increasing, allowing surgical procedures previously considered impossible or too risky due to their complexity or limited access. These reliable tools have improved surgical efficiency and safety. Cancer treatment is one of the surgical specialties that has benefited most from these techniques due to its high incidence and the accuracy required for tumor resections with conservative approaches and clear margins. However, in many cases, introducing these technologies into surgical scenarios is expensive and entails complex setups that are obtrusive, invasive, and increase the operative time. In this thesis, we proposed convenient, accessible, reliable, and non-invasive solutions for two highly complex regions for tumor resection surgeries: pelvis and head and neck. We explored how the introduction of 3D printing, surgical navigation, and augmented reality in these scenarios provided high intraoperative precision. First, we presented a less invasive setup for osteotomy guidance in pelvic tumor resections based on small patient-specific instruments (PSIs) fabricated with a desktop 3D printer at a low cost. We evaluated their accuracy in a cadaveric study, following a realistic workflow, and obtained similar results to previous studies with more invasive setups. We also identified the ilium as the region more prone to errors. Then, we proposed surgical navigation using these small PSIs for image-to-patient registration. Artificial landmarks included in the PSIs substitute the anatomical landmarks and the bone surface commonly used for this step, which require additional bone exposure and is, therefore, more invasive. We also presented an alternative and more convenient installation of the dynamic reference frame used to track the patient movements in surgical navigation. The reference frame is inserted in a socket included in the PSIs and can be attached and detached without losing precision and simplifying the installation. We validated the setup in a cadaveric study, evaluating the accuracy and finding the optimal PSI configuration in the three most common scenarios for pelvic tumor resection. The results demonstrated high accuracy, where the main source of error was again incorrect placements of PSIs in regular and homogeneous regions such as the ilium. The main limitation of PSIs is the guidance error resulting from incorrect placements. To overcome this issue, we proposed augmented reality as a tool to guide PSI installation in the patient’s bone. We developed an application for smartphones and HoloLens 2 that displays the correct position intraoperatively. We measured the placement errors in a conventional and a realistic phantom, including a silicone layer to simulate tissue. The results demonstrated a significant reduction of errors with augmented reality compared to freehand placement, ensuring an installation of the PSI close to the target area. Finally, we proposed three setups for surgical navigation in palate tumor resections, using optical trackers and augmented reality. The tracking tools for the patient and surgical instruments were fabricated with low-cost desktop 3D printers and designed to provide less invasive setups compared to previous solutions. All setups presented similar results with high accuracy when tested in a 3D-printed patient-specific phantom. They were then validated in the real surgical case, and one of the solutions was applied for intraoperative guidance. Postoperative results demonstrated high navigation accuracy, obtaining optimal surgical outcomes. The proposed solution enabled a conservative surgical approach with a less invasive navigation setup. To conclude, in this thesis we have proposed new setups for intraoperative navigation in two complex surgical scenarios for tumor resection. We analyzed their navigation precision, defining the optimal configurations to ensure accuracy. With this, we have demonstrated that computer-assisted surgery techniques can be integrated into the surgical workflow with accessible and non-invasive setups. These results are a step further towards optimizing the procedures and continue improving surgical outcomes in complex surgical scenarios.Programa de Doctorado en Ciencia y TecnologĂ­a BiomĂ©dica por la Universidad Carlos III de MadridPresidente: RaĂșl San JosĂ© EstĂ©par.- Secretario: Alba GonzĂĄlez Álvarez.- Vocal: Simon Droui

    Mri-Based Radiomics in Breast Cancer:Optimization and Prediction

    Get PDF

    Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

    Get PDF
    Aim To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates. Materials and methods The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≄3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud Conclusion This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management

    Identification of cancer hallmarks in patients with non-metastatic colon cancer after surgical resection

    Get PDF
    Colon cancer is one of the most common cancers in the world, and the therapeutic workflow is dependent on the TNM staging system and the presence of clinical risk factors. However, in the case of patients with non-metastatic disease, evaluating the benefit of adjuvant chemotherapy is a clinical challenge. Radiomics could be seen as a non-invasive novel imaging biomarker able to outline tumor phenotype and to predict patient prognosis by analyzing preoperative medical images. Radiomics might provide decisional support for oncologists with the goal to reduce the number of arbitrary decisions in the emerging era of personalized medicine. To date, much evidence highlights the strengths of radiomics in cancer workup, but several aspects limit the use of radiomics methods as routine. The study aimed to develop a radiomic model able to identify high-risk colon cancer by analyzing pre-operative CT scans. The study population comprised 148 patients: 108 with non-metastatic colon cancer were retrospectively enrolled from January 2015 to June 2020, and 40 patients were used as the external validation cohort. The population was divided into two groups—High-risk and No-risk—following the presence of at least one high-risk clinical factor. All patients had baseline CT scans, and 3D cancer segmentation was performed on the portal phase by two expert radiologists using open-source software (3DSlicer v4.10.2). Among the 107 radiomic features extracted, stable features were selected to evaluate the inter-class correlation (ICC) (cut-off ICC > 0.8). Stable features were compared between the two groups (T-test or Mann–Whitney), and the significant features were selected for univariate and multivariate logistic regression to build a predictive radiomic model. The radiomic model was then validated with an external cohort. In total, 58/108 were classified as High-risk and 50/108 as No-risk. A total of 35 radiomic features were stable (0.81 ≀ ICC <  0.92). Among these, 28 features were significantly different between the two groups (p < 0.05), and only 9 features were selected to build the radiomic model. The radiomic model yielded an AUC of 0.73 in the internal cohort and 0.75 in the external cohort. In conclusion, the radiomic model could be seen as a performant, non-invasive imaging tool to properly stratify colon cancers with high-risk diseas
    • 

    corecore