2,829 results match your criteria dice coefficient

Multi-level Kronecker Convolutional Neural Network (ML-KCNN) for Glioma Segmentation from Multi-modal MRI Volumetric Data.

J Digit Imaging 2021 Jul 29. Epub 2021 Jul 29.

Medical Imaging and Diagnostic Lab, National Centre of Artificial Intelligence, Department of Computer Science, COMSATS University Islamabad (CUI), 45550, Islamabad, Pakistan.

The development of an automated glioma segmentation system from MRI volumes is a difficult task because of data imbalance problem. The ability of deep learning models to incorporate different layers for data representation assists medical experts like radiologists to recognize the condition of the patient and further make medical practices easier and automatic. State-of-the-art deep learning algorithms enable advancement in the medical image segmentation area, such a segmenting the volumes into sub-tumor classes. Read More

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Volumetric Accuracy Analysis of Virtual Safety Barriers for Cooperative-Control Robotic Mastoidectomy.

Otol Neurotol 2021 Jul 28. Epub 2021 Jul 28.

Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine Department of Biomedical Engineering Department of Computer Science, Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland.

Hypothesis: Virtual fixtures can be enforced in cooperative-control robotic mastoidectomies with submillimeter accuracy.

Background: Otologic procedures are well-suited for robotic assistance due to consistent osseous landmarks. We have previously demonstrated the feasibility of cooperative-control robots (CCRs) for mastoidectomy. Read More

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MotilityJ: An open-source tool for the classification and segmentation of bacteria on motility images.

Comput Biol Med 2021 Jul 21;136:104673. Epub 2021 Jul 21.

Area de Microbiología Molecular, Centro de Investigación Biomédica de La Rioja (CIBIR), Logroño, Spain.

Background And Objectives: Infectious diseases produced by antimicrobial resistant microorganisms are a major threat to human, and animal health worldwide. This problem is increased by the virulence and spread of these bacteria. Surface motility has been regarded as a pathogenicity element because it is essential for many biological functions, but also for disease spreading; hence, investigations on the motility behaviour of bacteria are crucial to understand chemotaxis, biofilm formation and virulence in general. Read More

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MRI-TRUS registration methodology for TRUS-guided HDR prostate brachytherapy.

J Appl Clin Med Phys 2021 Jul 28. Epub 2021 Jul 28.

Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.

Purpose: High-dose-rate (HDR) prostate brachytherapy is an established technique for whole-gland treatment. For transrectal ultrasound (TRUS)-guided HDR prostate brachytherapy, image fusion with a magnetic resonance image (MRI) can be performed to make use of its soft-tissue contrast. The MIM treatment planning system has recently introduced image registration specifically for HDR prostate brachytherapy and has incorporated a Predictive Fusion workflow, which allows clinicians to attempt to compensate for differences in patient positioning between imaging modalities. Read More

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Deep Learning Network for Segmentation of the Prostate Gland With Median Lobe Enlargement in T2-weighted MR Images: Comparison With Manual Segmentation Method.

Curr Probl Diagn Radiol 2021 Jul 11. Epub 2021 Jul 11.

Section of Radiology - BiND, University Hospital "Paolo Giaccone", Via del Vespro 129, 90127, Palermo, Italy.

Purpose: Aim of this study was to evaluate a fully automated deep learning network named Efficient Neural Network (ENet) for segmentation of prostate gland with median lobe enlargement compared to manual segmentation.

Materials And Methods: One-hundred-three patients with median lobe enlargement on prostate MRI were retrospectively included. Ellipsoid formula, manual segmentation and automatic segmentation were used for prostate volume estimation using T2 weighted MRI images. Read More

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Automatic segmentation of brain metastases using T1 magnetic resonance and computed tomography images.

Phys Med Biol 2021 Jul 27. Epub 2021 Jul 27.

Memorial Sloan Kettering Cancer Center, New York, New York, UNITED STATES.

An increasing number of patients with multiple brain metastases are being treated with stereotactic radiosurgery (SRS). Manually identifying and contouring all metastatic lesions is difficult and time-consuming, and a potential source of variability. Hence, we developed a 3D deep learning approach for segmenting brain metastases on MR and CT images. Read More

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Multimodality-based knee joint modeling method with bone and cartilage structures for total knee arthroplasty.

Int J Med Robot 2021 Jul 26:e2316. Epub 2021 Jul 26.

School of Mechanical Engineering and Automation, Beihang University, Beijing, China.

Objective: We propose a robust and accurate knee joint modeling method with bone and cartilage structures to enable accurate surgical guidance for knee surgery.

Methods: A multimodality registration strategy is proposed to fuse MR and CT images of the femur and tibia separately to remove spatial inconsistency caused by knee bending in CT/MR scans. Automatic segmentation of the femur, tibia, and cartilages is carried out with ROI clustering and intensity analysis based on the multimodal fusion of images. Read More

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Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT.

Clin Oral Investig 2021 Jul 27. Epub 2021 Jul 27.

Center for TMD and Orofacial Pain, Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, No. 22 Zhong Guan Cun South Ave, Beijing, 100081, People's Republic of China.

Objectives: The objective of our study was to develop and validate a deep learning approach based on convolutional neural networks (CNNs) for automatic detection of the mandibular third molar (M3) and the mandibular canal (MC) and evaluation of the relationship between them on CBCT.

Materials And Methods: A dataset of 254 CBCT scans with annotations by radiologists was used for the training, the validation, and the test. The proposed approach consisted of two modules: (1) detection and pixel-wise segmentation of M3 and MC based on U-Nets; (2) M3-MC relation classification based on ResNet-34. Read More

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Segmentation of common and internal carotid arteries from 3D ultrasound images based on adaptive triple loss.

Med Phys 2021 Jul 26. Epub 2021 Jul 26.

Department of Electrical Engineering, City University of Hong Kong, Hong Kong.

Purpose: Vessel-wall-volume (VWV) and localized vessel-wall-thickness (VWT) measured from 3D ultrasound (US) carotid images are sensitive to anti-atherosclerotic effects of medical/dietary treatments. VWV and VWT measurements require the lumen-intima (LIB) and media-adventitia boundaries (MAB) at the common and internal carotid arteries (CCA and ICA). However, most existing segmentation techniques were capable of segmenting the CCA only. Read More

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Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning.

Phys Imaging Radiat Oncol 2021 Jul 2;19:39-44. Epub 2021 Jul 2.

Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.

Background And Purpose: Segmentation of oropharyngeal squamous cell carcinoma (OPSCC) is needed for radiotherapy planning. We aimed to segment the primary tumor for OPSCC on MRI using convolutional neural networks (CNNs). We investigated the effect of multiple MRI sequences as input and we proposed a semi-automatic approach for tumor segmentation that is expected to save time in the clinic. Read More

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Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures.

PeerJ Comput Sci 2021 29;7:e607. Epub 2021 Jun 29.

Smart Systems, AI and Cybersecurity Research Centre, Staffordshire University, Stoke on Trent, UK.

Medical imaging refers to visualization techniques to provide valuable information about the internal structures of the human body for clinical applications, diagnosis, treatment, and scientific research. Segmentation is one of the primary methods for analyzing and processing medical images, which helps doctors diagnose accurately by providing detailed information on the body's required part. However, segmenting medical images faces several challenges, such as requiring trained medical experts and being time-consuming and error-prone. Read More

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Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion.

Front Neurosci 2021 9;15:705323. Epub 2021 Jul 9.

College of Computer Science and Technology, Qingdao University, Qingdao, China.

The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Read More

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Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI.

Sci Rep 2021 Jul 23;11(1):15065. Epub 2021 Jul 23.

Department of Orthopaedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Occupation ratio and fatty infiltration are important parameters for evaluating patients with rotator cuff tears. We analyzed the occupation ratio using a deep-learning framework and studied the fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. To calculate the amount of fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. Read More

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Evaluation of Deep Learning-based Approaches to Segment Bowel Air Pockets and Generate Pelvis Attenuation Maps from CAIPIRINHA-accelerated Dixon MR Images.

J Nucl Med 2021 Jul 22. Epub 2021 Jul 22.

Athinoula A. Martinos Center for Biomedical Imaging, United States.

Attenuation correction (AC) remains a challenge in pelvis PET/MR imaging. In addition to the segmentation/model-based approaches, deep learning methods have shown promise in synthesizing accurate pelvis attenuation maps (μ-maps). However, these methods often misclassify air pockets in the digestive tract, which can introduce bias in the reconstructed PET images. Read More

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A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors.

Sensors (Basel) 2021 Jul 20;21(14). Epub 2021 Jul 20.

BioImaging Lab, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA.

Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Read More

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Evaluation of T2-Weighted MRI for Visualization and Sparing of Urethra with MR-Guided Radiation Therapy (MRgRT) On-Board MRI.

Cancers (Basel) 2021 Jul 16;13(14). Epub 2021 Jul 16.

Physics and Biology in Medicine IDP, University of California, 650 Charles E Young Drive S, Los Angeles, CA 90095, USA.

Purpose: To evaluate urethral contours from two optimized urethral MRI sequences with an MR-guided radiotherapy system (MRgRT).

Methods: Eleven prostate cancer patients were scanned on a MRgRT system using optimized urethral 3D HASTE and 3D TSE. A resident radiation oncologist contoured the prostatic urethra on the patients' planning CT, diagnostic 3T T2w MRI, and both urethral MRIs. Read More

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CT to MR registration of complex deformations in the knee joint through dual quaternion interpolation of rigid transforms.

Phys Med Biol 2021 Jul 23. Epub 2021 Jul 23.

Image Sciences Institute, University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Room Q0S.459 , Utrecht , The Netherlands, Utrecht, NETHERLANDS.

Purpose: To develop a method that enables CT to MR image registration of complex deformations typically encountered in rotating joints such as the knee joint.

Methods: We propose a workflow, denoted Quaternion Interpolated Registration (QIR), consisting of three steps, which uses prior knowledge of tissue properties to initialise deformable registration. First, the rigid skeletal components were individually registered. Read More

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Deep Learning-Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes.

Transl Vis Sci Technol 2021 07;10(8):21

Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA.

Purpose: To design a robust and automated estimation method for measuring the retinal nerve fiber layer (RNFL) thickness using spectral domain optical coherence tomography (SD-OCT).

Methods: We developed a deep learning-based image segmentation network for automated segmentation of the RNFL in SD-OCT B-scans of mouse eyes. In total, 5500 SD-OCT B-scans (5200 B-scans were used as training data with the remaining 300 B-scans used as testing data) were used to develop this segmentation network. Read More

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Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT.

Radiat Oncol 2021 Jul 22;16(1):135. Epub 2021 Jul 22.

Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan.

Background: This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvic region.

Methods: A total of 470 prostate cancer patients who had undergone intensity-modulated radiotherapy or volumetric-modulated arc therapy were enrolled. Our model was based on FusionNet, a fully residual deep CNN developed to semantically segment biological images. Read More

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Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning.

Phys Med Biol 2021 Jul 22. Epub 2021 Jul 22.

Radiation Oncology, Beaumont Health System, Royal Oak, Michigan, UNITED STATES.

Purpose: To develop and evaluate the performance of a deep learning model to generate synthetic pulmonary perfusion images from clinical 4DCT images for patients undergoing radiotherapy for lung cancer.

Methods: A clinical data set of 58 pre- and post-radiotherapy 99mTc-labelled MAA-SPECT perfusion studies (32 patients) each with contemporaneous 4DCT studies was collected. Using the inhale and exhale phases of the 4DCT, a 3D-Residual Network was trained to create synthetic perfusion images utilizing the MAA-SPECT as ground truth. Read More

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The predictive value of segmentation metrics on dosimetry in organs at risk of the brain.

Med Image Anal 2021 Jul 13;73:102161. Epub 2021 Jul 13.

ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland. Electronic address:

Background: Fully automatic medical image segmentation has been a long pursuit in radiotherapy (RT). Recent developments involving deep learning show promising results yielding consistent and time efficient contours. In order to train and validate these systems, several geometric based metrics, such as Dice Similarity Coefficient (DSC), Hausdorff, and other related metrics are currently the standard in automated medical image segmentation challenges. Read More

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3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning.

Comput Methods Programs Biomed 2021 Jul 8;208:106261. Epub 2021 Jul 8.

Department of Radiology, University of Cambridge, CB2 0QQ Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090 Vienna, Austria.

Background And Objectives: Deep learning is being increasingly used for deformable image registration and unsupervised approaches, in particular, have shown great potential. However, the registration of abdominopelvic Computed Tomography (CT) images remains challenging due to the larger displacements compared to those in brain or prostate Magnetic Resonance Imaging datasets that are typically considered as benchmarks. In this study, we investigate the use of the commonly used unsupervised deep learning framework VoxelMorph for the registration of a longitudinal abdominopelvic CT dataset acquired in patients with bone metastases from breast cancer. Read More

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Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates.

Int J Comput Assist Radiol Surg 2021 Jul 21. Epub 2021 Jul 21.

Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Room 805, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.

Purpose: In cranio-maxillofacial surgery, it is of great clinical significance to segment mandible accurately and automatically from CT images. However, the connected region and blurred boundary in teeth and condyles make the process challenging. At present, the mandible is commonly segmented by experienced doctors using manually or semi-automatic methods, which is time-consuming and has poor segmentation consistency. Read More

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Inter-observer variations of the tumor bed delineation for patients after breast conserving surgery in preoperative magnetic resonance and computed tomography scan fusion.

BMC Cancer 2021 Jul 20;21(1):838. Epub 2021 Jul 20.

Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, 250117, Shandong Province, China.

Purpose: Tumor bed (TB) delineation based on preoperative magnetic resonance imaging (pre-MRI) fused with postoperative computed tomography (post-CT) were compared to post-CT only to define pre-MRI may aid in improving the accuracy of delineation.

Methods And Materials: The pre-MRI imaging of 10 patients underwent radiotherapy (RT) after breast conserving surgery (BCS) were reviewed. Post-CT scans were acquired in the same prone position as pre-MRI. Read More

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O-Net: An Overall Convolutional Network for Segmentation Tasks.

Mach Learn Med Imaging 2020 Oct 29;12436:199-209. Epub 2020 Sep 29.

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Convolutional neural networks (CNNs) have recently been popular for classification and segmentation through numerous network architectures offering a substantial performance improvement. Their value has been particularly appreciated in the domain of biomedical applications, where even a small improvement in the predicted segmented region (e.g. Read More

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October 2020

United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI.

Med Image Anal 2021 Jun 29;73:102154. Epub 2021 Jun 29.

Department of Medical Imaging, Western University, London, ON, Canada; Digital Imaging Group of London, London, ON, Canada. Electronic address:

Simultaneous segmentation and detection of liver tumors (hemangioma and hepatocellular carcinoma (HCC)) by using multi-modality non-contrast magnetic resonance imaging (NCMRI) are crucial for the clinical diagnosis. However, it is still a challenging task due to: (1) the HCC information on NCMRI is insufficient makes extraction of liver tumors feature difficult; (2) diverse imaging characteristics in multi-modality NCMRI causes feature fusion and selection difficult; (3) no specific information between hemangioma and HCC on NCMRI cause liver tumors detection difficult. In this study, we propose a united adversarial learning framework (UAL) for simultaneous liver tumors segmentation and detection using multi-modality NCMRI. Read More

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Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis.

J Clin Neurosci 2021 Aug 24;90:60-67. Epub 2021 May 24.

Department of Neuroradiology, Department of Radiology, Taichung Veterans General Hospital, 1650 Sect. 4 Taiwan Boulevard, Taichung 40705, Taiwan, ROC.

Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This study aims to develop an automated method of aqueduct CSF flow analysis using convolution neural networks (CNNs), which can replace the current standard involving manual segmentation of aqueduct region of interest (ROI). Retrospective analysis was performed on 333 patients who underwent PC-MRI, totaling 353 imaging studies. Read More

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Incorporating the hybrid deformable model for improving the performance of abdominal CT segmentation via multi-scale feature fusion network.

Med Image Anal 2021 Jul 9;73:102156. Epub 2021 Jul 9.

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China. Electronic address:

Automated multi-organ abdominal Computed Tomography (CT) image segmentation can assist the treatment planning, diagnosis, and improve many clinical workflows' efficiency. The 3-D Convolutional Neural Network (CNN) recently attained state-of-the-art accuracy, which typically relies on supervised training with many manual annotated data. Many methods used the data augmentation strategy with a rigid or affine spatial transformation to alleviate the over-fitting problem and improve the network's robustness. Read More

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SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography.

Comput Methods Programs Biomed 2021 Jul 6;208:106268. Epub 2021 Jul 6.

Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan University of Technology and Business, Changsha 410205, China. Electronic address:

Background And Objective: Liver segmentation is an essential prerequisite for liver cancer diagnosis and surgical planning. Traditionally, liver contour is delineated manually by radiologist in a slice-by-slice fashion. However, this process is time-consuming and prone to errors depending on radiologist's experience. Read More

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Precise segmentation of non-enhanced computed tomography in patients with ischemic stroke based on multi-scale U-Net deep network model.

Comput Methods Programs Biomed 2021 Jul 9;208:106278. Epub 2021 Jul 9.

Department of Neurosurgery, Cangzhou Central Hospital, Hebei 061000, China.

Background And Objective: Acute ischemic stroke requires timely diagnosis and thrombolytic therapy, but it is difficult to locate and quantify the lesion site manually. The purpose of this study was to explore a more rapid and effective method for automatic image segmentation of acute ischemic stroke.

Methods: The image features of 30 stroke patients were segmented from non-enhanced computed tomography (CT) images using a multi-scale U-Net deep network model. Read More

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