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检索条件"任意字段=Conference on Medical Imaging - Image-Guided Procedures, Robotic Interventions and Modeling"

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A personalized approach for microwave ablation treatment planning fusing radiomics and bioheat transfer modeling

A personalized approach for microwave ablation treatment pla...

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medical imaging conference - image-guided procedures, robotic interventions, and modeling

作者： Taeprasartsit, Pinyo Pathompatai, Chanok Jusomjai, Kasidit Wibowo, Henky Sebek, Jan Prakash, Punit Silpakorn Univ Fac Sci Dept Comp Bangkok 73000 Thailand Kansas State Univ Dept Elect & Comp Engn Manhattan KS 66506 USA PhenoMapper LLC San Jose CA 95120 USA Czech Tech Univ Dept Circuit Theory Prague Czech Republic

ISBN: (纸本)9781510633988

The use of percutaneous and bronchoscopic microwave ablation to treat both primary and secondary lung tumors has been growing recently. These ablation systems are typically accompanied by an ablation planning system to optimize the treatment outcome by ensuring adequate margin in the expected ablation zone during the planning phase. The planning system utilizes pre-operative CT scan to identify the tumor and recommend microwave probe position. Radiomics is a process of converting medical images into higher-dimensional data and subsequent mining of data to reveal underlying pathophysiology for enhancing clinical decision support making Radiomics analysis have shown promises in capturing distinct tumor characteristics and predicting prognosis of the tumor. Here, we present a new method to predict microwave ablation zones by supplementing a bioheat transfer model of microwave tissue ablation with microwave sensitive radiomics features. We hypothesize that supplementing traditional bioheat transfer modeling with microwave sensitive radiomics features will generate a more accurate and personalized ablation prediction that will lead to better treatment outcome. Inputs to the bioheat transfer modeling approach include the geometry of the target tumor, physical characteristics of the tissue, and dimensions of the microwave ablation applicator. The radiomics algorithm extracts characteristics of the targeted tumor's size and shape, as well as texture characteristics, from pre-operative CT images. We employed cascaded segmentation based on RetinaNet and U-Net to obtain a tumor's size and shape. Then, a segmented tumor is employed for texture analysis through a set of regression convolutional neural networks. These tumor characteristics are employed as radiomics features for more accurate dose prediction and margin for microwave ablation treatment. We present the preliminary results of a study using images from clinical lung tumor cases to predict ablation treatment outcome, w

关键词： radiomics microwave ablation dose prediction

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Hyperspectral imaging for tissue classification in glioblastoma tumor patients: a deep spectral-spatial approach

Hyperspectral imaging for tissue classification in glioblast...

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conference on medical imaging - image-guided procedures, robotic interventions, and modeling

作者： Manni, Francesca Cai, Chuchen van der Sommen, Fons Zinger, Svitlana Shan, Caifeng Edstrom, Erik Elmi-Terander, Adrian Fabelo, Himar Ortega, Samuel Callico, Gustavo M. de With, Peter H. N. Eindhoven Univ Technol Eindhoven Netherlands Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China Karolinska Inst Dept Clin Neurosci Stockholm Sweden Karolinska Univ Hosp Dept Neurosurg Stockholm Sweden Univ Las Palmas Gran Canaria Inst Appl Microelect Las Palmas Gran Canaria Spain

ISBN: (纸本)9781510640269

Surgery is a crucial treatment for malignant brain tumors where gross total resection improves the prognosis. Tissue samples taken during surgery are either subject to a preliminary intraoperative histological analysis, or sent for a full pathological evaluation which can take days or weeks. Whereas a lengthy complete pathological analysis includes an array of techniques to be executed, a preliminary tissue analysis on frozen tissue is performed as quickly as possible (30-45 minutes on average) to provide fast feedback to the surgeon during the surgery. The surgeon uses the information to confirm that the resected tissue is indeed tumor and may, at least in theory, initiate repeated biopsies to help achieve gross total resection. However, due to the total turn-around time of the tissue inspection for repeated analyses, this approach may not be feasible during a single surgery. In this context, intraoperative image-guided techniques can improve the clinical workflow for tumor resection and improve outcome by aiding in the identification and removal of the malignant lesion. Hyperspectral imaging (HSI) is an optical imaging technique with the potential to extract combined spectral-spatial information. By exploiting HSI for human brain-tissue classification in 13 in-vivo hyperspectral images from 9 patients, a brain-tissue classifier is developed. The framework consists of a hybrid 3D-2D CNN-based approach and a band-selection step to enhance the capability of extracting both spectral and spatial information from the hyperspectral images. An overall accuracy of 77% was found when tumor, normal and hyper-vascularized tissue are classified, which clearly outperforms the state-of-the-art approaches (SVM, 2D-CNN). These results may open an attractive future perspective for intraoperative brain-tumor classification using HSI.

关键词： Hyperspectral imaging malignant brain tumor ant colony optimization 3D-2D convolutional neural network (CNN)

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Pixel-level Tumor Margin Assessment of Surgical Specimen with Hyperspectral imaging and Deep Learning Classification

Pixel-level Tumor Margin Assessment of Surgical Specimen wit...

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conference on medical imaging - image-guided procedures, robotic interventions, and modeling

作者： Ma, Ling Shahedi, Maysam Shi, Ted Halicek, Martin Little, James, V Chen, Amy Y. Myers, Larry L. Sumer, Baran D. Fei, Baowei Univ Texas Dallas Dept Bioengn Richardson TX 75083 USA Tianjin Univ State Key Lab Precis Measurement Technol & Instru Tianjin Peoples R China Emory Univ Dept Pathol & Lab Med Atlanta GA 30322 USA Emory Univ Dept Otolaryngol Atlanta GA 30322 USA Univ Texas Southwestern Med Ctr Dept Otolaryngol Dallas TX USA Univ Texas Southwestern Med Ctr Adv Imaging Res Ctr Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Dept Radiol Dallas TX 75390 USA

ISBN: (纸本)9781510640269

Surgery is a major treatment method for squamous cell carcinoma (SCC). During surgery, insufficient tumor margin may lead to local recurrence of cancer. Hyperspectral imaging (HSI) is a promising optical imaging technique for in vivo cancer detection and tumor margin assessment. In this study, a fully convolutional network (FCN) was implemented for tumor classification and margin assessment on hyperspectral images of SCC. The FCN was trained and validated with hyperspectral images of 25 ex vivo SCC surgical specimens from 20 different patients. The network was evaluated per patient and achieved pixel-level tissue classification with an average area under the curve (AUC) of 0.88, as well as 0.83 accuracy, 0.84 sensitivity, and 0.70 specificity across all the 20 patients. The 95% Hausdorff distance of assessed tumor margin in 17 patients was less than 2 mm, and the classification time of each tissue specimen took less than 10 seconds. The proposed methods can potentially facilitate intraoperative tumor margin assessment and improve surgical outcomes.

关键词： Hyperspectral imaging U-Net squamous cell carcinoma tumor margin assessment classification

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Three-dimensional prostate CT segmentation through fine-tuning of a pre-trained neural network using no reference labeling

Three-dimensional prostate CT segmentation through fine-tuni...

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conference on medical imaging - image-guided procedures, robotic interventions, and modeling

作者： Caughlin, Kayla Shahedi, Maysam Shoag, Jonathan E. Barbieri, Christopher Margolis, Daniel Fei, Baowei Univ Texas Dallas Dept Bioengn Richardson TX 75083 USA Weill Cornell Med Dept Urol New York NY USA Case Western Reserve Univ Univ Hosp Med Ctr Dept Urol Cleveland OH 44106 USA Weill Cornell Med Dept Radiol New York NY USA Univ Texas Southwestern Med Ctr Dallas Adv Imaging Res Ctr Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Dallas Dept Radiol Dallas TX 75390 USA

ISBN: (纸本)9781510640269

Accurate segmentation of the prostate on computed tomography (CT) has many diagnostic and therapeutic applications. However, manual segmentation is time-consuming and suffers from high inter- and intra-observer variability. Computer-assisted approaches are useful to speed up the process and increase the reproducibility of the segmentation. Deep learning-based segmentation methods have shown potential for quick and accurate segmentation of the prostate on CT images. However, difficulties in obtaining manual, expert segmentations on a large quantity of images limit further progress. Thus, we proposed an approach to train a base model on a small, manually-labeled dataset and fine-tuned the model using unannotated images from a large dataset without any manual segmentation. The datasets used for pre-training and fine-tuning the base model have been acquired in different centers with different CT scanners and imaging parameters. Our fine-tuning method increased the validation and testing Dice scores. A paired, two-tailed t-test shows a significant change in test score (p = 0.017) demonstrating that unannotated images can be used to increase the performance of automated segmentation models.

关键词： image segmentation deep learning pre-trained neural networks fine tuning prostate computed tomography (CT)

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A Quantitative Prediction of the Post-operative Lobectomy Lung Physiology using a GPU-based Linear Elastic Lung Biomechanics Model and a Constrained Generative Adversarial Learning Approach

A Quantitative Prediction of the Post-operative Lobectomy Lu...

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conference on medical imaging - image-guided procedures, robotic interventions, and modeling

作者： Santhanam, Anand Stiehl, Brad Lauria, Michael Barjaktarevic, Igor Goldin, Jonathan Yanagawa, Jane Low, Daniel Univ Calif Los Angeles Dept Radiat Oncol Los Angeles CA 90024 USA Univ Calif Los Angeles Div Pulm & Crit Care Los Angeles CA USA Univ Calif Los Angeles Div Radiol Los Angeles CA USA Univ Calif Los Angeles Div Thorac Surg Los Angeles CA USA

ISBN: (纸本)9781510640269

Lobectomy is a common and effective procedure for treating early-stage lung cancers. However, for patients with compromised pulmonary function (e.g. COPD) lobectomy can lead to major postoperative pulmonary complications. A technique for quantitatively predicting postoperative pulmonary function is needed to assist surgeons in assessing candidate's suitability for lobectomy. We present a framework for quantitatively predicting the postoperative lung physiology and function using a combination of lung biomechanical modeling and machine learning strategies. A set of 10 patients undergoing lobectomy was used for this purpose. The image input consists of pre- and post-operative breath hold CTs. An automated lobe segmentation algorithm and lobectomy simulation framework was developed using a Constrained Adversarial Generative Networks approach. Using the segmented lobes, a patient-specific GPU-based linear elastic biomechanical and airflow model and surgery simulation was then assembled that quantitatively predicted the lung deformation during the forced expiration maneuver. The lobe in context was then removed by simulating a volume reduction and computing the elastic stress on the surrounding residual lobes and the chest wall. Using the deformed lung anatomy that represents the post-operative lung geometry, the forced expiratory volume in 1 second (FEV1) (the amount of air exhaled by a patient in 1 second starting from maximum inhalation), and forced vital capacity (FVC) (the amount of air exhaled by force from maximum inhalation), were then modeled. Our results demonstrated that the proposed approach quantitatively predicted the postoperative lobe-wise lung function at the FEV1 and FEV/FVC.

关键词： Lobectomy Lung Dynamics Lung Segmentation Biomechanical modeling Machine Learning

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MRI-compatible mechatronic needle guidance system for focal thermal laser ablation in localized prostate cancer

MRI-compatible mechatronic needle guidance system for focal ...

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conference on medical imaging - image-guided procedures, robotic interventions, and modeling

作者： Knull, Eric Parkb, Claire K. Baxb, Jeffrey Tessierb, David Fenstera, Aaron Western Univ Sch Biomed Engn Fac Engn London ON N6A 3K7 Canada Western Univ Imaging Res Labs Robarts Res Inst London ON N6A 3K7 Canada Western Univ Dept Med Biophys Schulich Sch Med & Dent London ON N6A 3K7 Canada

ISBN: (纸本)9781510640269

Prostate cancer is the most frequently diagnosed non-cutaneous cancer and the second leading cause of cancer-related deaths in men. Whole gland surgical and radiation treatments for prostate cancer are highly effective for long-term cancer control. However, these are often associated with overtreatment, resulting in urinary complications and sexual dysfunction, adversely impacting the quality of life. Focal laser ablation (FLA) under magnetic resonance imaging (MRI)-guidance is an alternative minimally invasive treatment method for localized prostate tumors while preserving surrounding structures and healthy tissues. Accurate needle positioning and delivery are critical for the therapeutic success of MRI-guided FLA. We propose an MRI-compatible mechatronic system for in-bore transperineal FLA needle guidance to localized prostate lesions. This paper presents the mechatronic system design, including a remotely actuated, four degree-of-freedom transperineal positioning and needle guidance mechanism, and adaptable needle guide. We demonstrate its MR compatibility and evaluated its mechanical bias in free-space testing using an external optical tracking system with several measurement points (N=40) over its range-of-motion. Free-space testing resulted in a root-mean-square error of 0.71 +/- 0.30 mm. Within an MR environment, in-bore testing to virtual targets (N=10) with projected needle trajectories resulted in a mean needle tip error of 1.81 +/- 0.56 mm and needle trajectory error of 0.78 +/- 0.75 degrees. This suggests that localized ablation regions can be accurately targeted within 2.16 mm within 95% confidence. An extensive in-bore analysis and correction for systematic bias across the range-of-motion may improve this accuracy. This study shows that our proposed mechatronic needle guidance system may be a feasible alternative for accurate MR-guided FLA for localized prostate therapy.

关键词： image-guided therapy magnetic resonance imaging prostate cancer focal laser ablation focal therapy needle guidance targeting validation

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Three-dimensional ultrasound for monitoring knee inflammation and cartilage damage in osteoarthritis and rheumatoid arthritis

Three-dimensional ultrasound for monitoring knee inflammatio...

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medical imaging conference - image-guided procedures, robotic interventions, and modeling

作者： Papernick, Sam Gillies, Derek J. Appleton, Tom Fenster, Aaron Western Univ Dept Med Biophys London ON N6A 3K7 Canada Western Univ Robarts Res Inst London ON N6A 5B7 Canada Western Univ Dept Med London ON N6A 5C1 Canada Western Univ Dept Physiol & Pharmacol London ON N6A 3K7 Canada

ISBN: (纸本)9781510633988

The most common chronic inflammatory and chronic joint diseases in Canada are rheumatoid arthritis (RA) and osteoarthritis (OA) respectively. The current methods for monitoring the development of these diseases and their response to treatment involve acquiring x-ray and magnetic resonance imaging (MRI) images and comparing the results to the patient's symptoms during examination However, x-ray imaging is associated with difficulties when trying to interpret 3D anatomy, and MRI is associated with high operating costs, long waitlists, long scan times, and is inaccessible to mobility-impaired patients. Our solution to these limitations is the use of three-dimensional ultrasound (3D US) imaging for providing bedside monitoring of RA and OA progression and their response to treatment. This project validates two 3D US acquisition devices: tilt acquisition and linear acquisition scanning methods. The linear and volumetric measurement capabilities were validated through scanning phantoms and segmenting resulting images at repeated time points. A proof-of-concept volunteer scan was conducted to compare the capabilities of 3D US against MRI for measuring articular cartilage volumes. Results indicated that the linear measurement errors for both the tilt and linear scanners were <5% of the known phantom dimensions. The volumetric measurement errors were <5% for the linear scanner and >10% (29.84%) for the tilt scanner. The percent difference between the volumes of the articular cartilage measured using 3D US and MRI of a healthy volunteer's knee was 6.46%. The linear scanner is therefore better suited for clinical scanning than the tilt scanner due to its smaller linear and volumetric errors.

关键词： 3D ultrasound rheumatoid arthritis osteoarthritis knee imaging 3D segmentation articular cartilage synovium measurement validation

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Deep Learning-based Volumetric image Generation from Projection imaging for Prostate Radiotherapy

Deep Learning-based Volumetric Image Generation from Project...

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conference on medical imaging - image-guided procedures, robotic interventions, and modeling

作者： Dai, Xianjin Lei, Yang Tian, Zhen Wang, Tonghe Liu, Tian Curran, Walter J. Yang, Xiaofeng Emory Univ Dept Radiat Oncol Atlanta GA 30322 USA Emory Univ Winship Canc Inst Atlanta GA 30322 USA

ISBN: (纸本)9781510640269

Due to the inter-fraction variation of anatomy, it is highly desired to provide fast and low-dose volumetric imaging during prostate radiation therapy treatment for patient setup and daily treatment dose estimation. In this study, we propose a novel generative adversarial network integrated with perceptual supervision to derive 3D volumetric images from two orthogonal 2D projections. Our proposed network, named TransNet, consists of three modules, i.e., encoding, transformation and decoding modules. Rather than only using image distance loss between the generated 3D images and the ground truth 3D CT images to supervise the network, adversarial loss is used to improve the realism of generated 3D images. We conducted a study on 20 patient cases, who had received prostate radiotherapy in our institution, and evaluated the efficacy and consistency of our method for two orthogonal projection angles, i.e., 0 degrees and 90 degrees. For each 3D CT image, we simulated its 2D projections at these two angles. The TransNet takes the two angles as input and output the 3D CT. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) achieved by our method are 117.5 +/- 15.3HU, 22.7 +/- 3.8dB and 0.904 +/- 0.27, respectively. These results demonstrate the feasibility and efficacy of our 2D-to- 3D method for prostate cancer patients, which provides a potential solution for fast on-board volumetric imaging for patient setup and adaptive radiation therapy.

关键词： Prostate radiotherapy CT volumetric image reconstruction deep learning

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Multi-Slot Intraoperative imaging and 3D-2D Registration for Evaluation of Long Surgical Constructs in Spine Surgery

Multi-Slot Intraoperative Imaging and 3D-2D Registration for...

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medical imaging conference - image-guided procedures, robotic interventions, and modeling

作者： Uneri, A. Zhang, X. Ketcha, M. D. Doerr, S. A. Jones, C. K. Helm, P. A. Siewerdsen, J. H. Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Medtronic Littleton MA USA

ISBN: (纸本)9781510633988

Purpose. Initial prototype for an intraoperative slot-scan imaging and registration technique is reported for 3D guidance and confirmation of surgical constructs in spine surgery specifically, involving placement of pedicle screws in multi-level spinal fusion or deformity correction. The unique projection geometry provided by a multi-slot collimator is exploited to produce parallax views of the radiographic scene for accurate 3D registration from a single scan. Methods. The approach takes advantage of the collimator apertures that form disparate fan-beam views to perform 3D 2D registration of 3D implant models (pedicle screws) and 2D raw detector slot-scan measurements acquired via linear motion of the gantry. Experiments using a prototype O-arm (Medtronic, Littleton MA) were conducted in a cadaver specimen to evaluate the geometric accuracy of multi-slot registration to that of conventional 3D-2D registration from multiple fluoroscopic views. Results. Cadaver studies showed the multi-slot apertures to provide a sufficient degree of parallax to estimate the 3D location of implants. Spinal pedicle screws were registered from a single slot-scan, with mean target registration error of <1.5 mm, comparable to the margins of errors for optical surgical tracking and dual radiograph (AP + Lat) registration. Conclusions. Presented work demonstrates the feasibility of using a multi-slot collimator to perform 3D pose estimation of the patient and surgical implants in intraoperative images. The method is particularly suitable to evaluating the quality of long surgical constructs, as in spinal deformity correction. Ongoing work includes radiation dosimetry in comparison to conventional radiography and streamlined integration with tools for automatic analysis of global spinal alignment.

关键词： intraoperative imaging slot-scan radiography image-guided surgery 3D-2D registration quality assurance

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Classification of Tumor Signatures from Electrosurgical Vapors using Mass Spectrometry and Machine Learning: A Feasibility Study

Classification of Tumor Signatures from Electrosurgical Vapo...

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medical imaging conference - image-guided procedures, robotic interventions, and modeling

作者： Connolly, Laura Jamzad, Amoon Kaufmann, Martin Rubino, Rachel Sedghi, Alireza Ungi, Tamas Asselin, Mark Yam, Scott Rudan, John Nicol, Christopher Fichtinger, Gabor Mousavi, Parvin Queens Univ Sch Comp Lab Percutaneous Surg Kingston ON Canada Queens Univ Sch Comp Med Informat Lab Kingston ON Canada Queens Univ Dept Elect & Comp Engn Kingston ON Canada Queens Univ Nicol Lab Kingston ON Canada Queens Univ Dept Surg Kingston ON Canada

ISBN: (纸本)9781510633988

PURPOSE: The iKnife is a new surgical tool designed to aid in tumor resection procedures by providing enriched chemical feedback about the tumor resection cavity from electrosurgical vapors. We build and compare machine learning classifiers that are capable of distinguishing primary cancer from surrounding tissue at different stages of tumor progression. In developing our classification framework, we implement feature reduction and recognition tools that will assist in the translation of xenograft studies to clinical application and compare these tools to standard linear methods that have been previously demonstrated. METHODS: Two cohorts (n=6 each) of 12 week old female immunocompromised (Rag2-/-;I12rg-/-) mice were injected with the same human breast adenocarcinoma (MDA-MB-231) cell line. At 4 and 6 weeks after cell injection, mice in each cohort were respectively euthanized, followed by iKnife burns performed on tumors and tissues prior to sample collection for future studies. A feature reduction technique that uses a neural network is compared to traditional linear analysis. For each method, we fit a classifier to distinguish primary cancer from surrounding tissue. RESULTS: Both classifiers can distinguish primary cancer from metastasis and surrounding tissue. The classifier that uses a neural network achieves an accuracy of 96.8% and the classifier without the neural network achieves an accuracy of 96%. CONCLUSIONS: The performance of these classifiers indicate that this device has the potential to offer real-time, intraoperative classification of tissue. This technology may be used to assist in intraoperative margin detection and inform surgical decisions to offer a better standard of care for cancer patients.

关键词： Tissues Tumors Neural networks Liver Cancer Data modeling Lung Machine learning Mass spectrometry Principal component analysis

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