A Novel Artificial Intelligence Based Denoising Method for Ultra-Low Dose CT Used for Lung Cancer Screening.

RATIONALE AND OBJECTIVES: To assess ultra-low-dose (ULD) computed tomography as well as a novel artificial intelligence-based reconstruction denoising method for ULD (dULD) in screening for lung cancer. MATERIALS AND METHODS: This prospective study included 123 patients, 84 (70.6%) men, mean age 62.6 ± 5.35 (55-75), who had a low dose and an ULD scan. A fully convolutional-network, trained using a unique perceptual loss was used for denoising. The network used for the extraction of the perceptual features was trained in an unsupervised manner on the data itself by denoising stacked auto-encoders. The perceptual features were a combination of feature maps taken from different layers of the network, instead of using a single layer for training. Two readers independently reviewed all sets of images. RESULTS: ULD decreased average radiation-dose by 76% (48%-85%). When comparing negative and actionable Lung-RADS categories, there was no difference between dULD and LD (p = 0.22 RE, p > 0.999 RR) nor between ULD and LD scans (p = 0.75 RE, p > 0.999 RR). ULD negative likelihood ratio (LR) for the readers was 0.033-0.097. dULD performed better with a negative LR of 0.021-0.051. Coronary artery calcifications (CAC) were documented on the dULD scan in 88(74%) and 81(68%) patients, and on the ULD in 74(62.2%) and 77(64.7%) patients. The dULD demonstrated high sensitivity, 93.9%-97.6%, with an accuracy of 91.7%. An almost perfect agreement between readers was noted for CAC scores: for LD (ICC = 0.924), dULD (ICC = 0.903), and for ULD (ICC = 0.817) scans. CONCLUSION: A novel AI-based denoising method allows a substantial decrease in radiation dose, without misinterpretation of actionable pulmonary nodules or life-threatening findings such as aortic aneurysms.

Liver Attenuation Assessment in Reduced Radiation Chest Computed Tomography.

OBJECTIVE: This study aimed to evaluate the reliability of liver and spleen Hounsfield units (HU) measurements in reduced radiation computed tomography (RRCT) of the chest within the sub-millisievert range. METHODS: We performed a prospective, institutional review board-approved study of accrued patients who underwent unenhanced normal-dose chest CT (NDCT) and with an average radiation dose of less than 5% of NDCT. In-house artificial intelligence-based denoising methods produced 2 denoised RRCT (dRRCT) series. Hepatic and splenic attenuations were measured on all 4 series: NDCT, RRCT, dRRCT1, and dRRCT2. Statistical analyses assessed the differences between the HU measurements of the liver and spleen in RRCTs and NDCT. As a test case, we assessed the performance of RRCTs for fatty liver detection, considering NDCT to be the reference standard. RESULTS: Wilcoxon test compared liver and spleen attenuation in the 72 patients included in our cohort. The liver attenuation in NDCT (median, 59.38 HU; interquartile range, 55.00-66.06 HU) was significantly different from the attenuation in RRCT, dRRCT1, and dRRCT2 (median, 63.63, 42.00, and 33.67 HU; interquartile range, 56.19-67.19, 37.33-45.83, and 30.33-38.50 HU, respectively), all with a P value <0.01. Six patients (8.3%) were considered to have fatty liver on NDCT. The specificity, sensitivity, and accuracy of fatty liver detection by RRCT were greater than 98.5%, 50%, and 94.3%, respectively. CONCLUSIONS: Attenuation measurements were significantly different between NDCT and RRCTs, but may still have diagnostic value in appreciating hepatosteastosis. Abdominal organ attenuation on RRCT protocols may differ from attenuation on NDCT and should be validated when new low-dose protocols are used.

Anisotropic neural deblurring for MRI acceleration.

PURPOSE: MRI has become the tool of choice for brain imaging, providing unrivalled contrast between soft tissues, as well as a wealth of information about anatomy, function, and neurochemistry. Image quality, in terms of spatial resolution and noise, is strongly dependent on acquisition duration. A typical brain MRI scan may last several minutes, with total protocol duration often exceeding 30 minutes. Long scan duration leads to poor patient experience, long waiting time for appointments, and high costs. Therefore, shortening MRI scans is crucial. In this paper, we investigate the enhancement of low-resolution (LR) brain MRI scanning, to enable shorter acquisition times without compromising the diagnostic value of the images. METHODS: We propose a novel fully convolutional neural enhancement approach. It is optimized for accelerated LR MRI acquisitions obtained by reducing the acquisition matrix size only along phase encoding direction. The network is trained to transform the LR acquisitions into corresponding high-resolution (HR) counterparts in an end-to-end manner. In contrast to previous neural-based MRI enhancement algorithms, such as DAGAN, the LR images used for training are real acquisitions rather than smoothed, downsampled versions of the HR images. RESULTS: The proposed method is validated qualitatively and quantitatively for an acceleration factor of 4. Favourable comparison is demonstrated against the state-of-the-art DeblurGAN and DAGAN algorithms in terms of PSNR and SSIM scores. The result was further confirmed by an image quality rating experiment performed by four senior neuroradiologists. CONCLUSIONS: The proposed method may become a valuable tool for scan time reduction in brain MRI. In continuation of this research, the validation should be extended to larger datasets acquired for different imaging protocols, and considering several MRI machines produced by different vendors.

Ultra-Low Dose Chest CT with Denoising for Lung Nodule Detection.

BACKGROUND: Medical imaging and the resultant ionizing radiation exposure is a public concern due to the possible risk of cancer induction. OBJECTIVES: To assess the accuracy of ultra-low-dose (ULD) chest computed tomography (CT) with denoising versus normal dose (ND) chest CT using the Lung CT Screening Reporting and Data System (Lung-RADS). METHODS: This prospective single-arm study comprised 52 patients who underwent both ND and ULD scans. Subsequently AI-based denoising methods were applied to produce a denoised ULD scan. Two chest radiologists independently and blindly assessed all scans. Each scan was assigned a Lung-RADS score and grouped as 1 + 2 and 3 + 4. RESULTS: The study included 30 men (58%) and 22 women (42%); mean age 69.9 ± 9 years (range 54-88). ULD scan radiation exposure was comparable on average to 3.6-4.8% of the radiation depending on patient BMI. Denoising increased signal-to-noise ratio by 27.7%. We found substantial inter-observer agreement in all scans for Lung-RADS grouping. Denoised scans performed better than ULD scans when negative likelihood ratio (LR-) was calculated (0.04--0.08 vs. 0.08-0.12). Other than radiation changes, diameter measurement differences and part-solid nodules misclassification as a ground-glass nodule caused most Lung-RADS miscategorization. CONCLUSIONS: When assessing asymptomatic patients for pulmonary nodules, finding a negative screen using ULD CT with denoising makes it highly unlikely for a patient to have a pulmonary nodule that requires aggressive investigation. Future studies of this technique should include larger cohorts and be considered for lung cancer screening as radiation exposure is radically reduced.

Fetal Brain Biometry: Is there an Agreement among Ultrasound, MRI and the Measurements at Birth?

PURPOSE: Measurement of the fetal brain can be achieved by different modalities, we aimed to assess the agreement between these methods and the head circumference at birth. METHODS: A retrospective study conducted between 2011-2018 at a tertiary referral medical center. Sonographic head circumference (HC), 2D MRI bi-parietal diameter (BPD) and occipito-frontal diameter (OFD), 3D MRI supra-tentorial volume (STV), and head circumference (HC) at birth were measured and converted into centiles according to gestational age. Spearman's rank correlation coefficient was used to assess the correlation between the modalities. RESULTS: A total of 88 fetuses were included. Mean gestational age at the time of fetal US and brain MRI acquisition were 34.4 ±â€¯2.8 and 34.6 ±â€¯2.6 weeks, respectively. A correlation was found between prenatal sonographic HC and the 3D MRI STV centiles (Rs = 0.859, p < 0.001), the BPD in 2D MRI (Rs = 0.813, p < 0.001), and the OFD in 2D MRI (Rs = 0.840, p < 0.001). Sonographic HC, OFD on 2D MRI, and STV on 3D MRI were all found to be correlated with the HC at birth (Rs = 0.865, p < 0.001; Rs 0.816, p < 0.001; Rs = 0.825, p < 0.001, respectively). CONCLUSIONS: There is a statistically significant agreement among the different prenatal clinically used modalities for measuring fetal brain and the head circumference at birth, however, this correlation is not perfect. Further study is needed to investigate the long-term prognosis of these fetuses.

Neural Segmentation of Seeding ROIs (sROIs) for Pre-Surgical Brain Tractography.

White matter tractography mapping is an important tool for neuro-surgical planning and navigation. It relies on the accurate manual delineation of anatomical seeding ROIs (sROIs) by neuroanatomy experts. Stringent pre-operative time-constraints and limited availability of experts suggest that automation tools are strongly needed for the task. In this article, we propose and compare several multi-modal fully convolutional network architectures for segmentation of sROIs. Inspired by their manual segmentation practice, anatomical information from T1w maps is fused by the network with directionally encoded color (DEC) maps to compute the segmentation. Qualitative and quantitative validation was performed on image data from 75 real tumor resection candidates for the sROIs of the motor tract, the arcuate fasciculus, and optic radiation. Favorable comparison was also obtained with state-of-the-art methods for the tumor dataset as well as the ISMRM 2017 traCED challenge dataset. The proposed networks showed promising results, indicating they may significantly improve the efficiency of pre-surgical tractography mapping, without compromising its quality.

Automatic detection and diagnosis of sacroiliitis in CT scans as incidental findings.

Early diagnosis of sacroiliitis may lead to preventive treatment which can significantly improve the patient's quality of life in the long run. Oftentimes, a CT scan of the lower back or abdomen is acquired for suspected back pain. However, since the differences between a healthy and an inflamed sacroiliac joint in the early stages are subtle, the condition may be missed. We have developed a new automatic algorithm for the diagnosis and grading of sacroiliitis CT scans as incidental findings, for patients who underwent CT scanning as part of their lower back pain workout. The method is based on supervised machine and deep learning techniques. The input is a CT scan that includes the patient's pelvis. The output is a diagnosis for each sacroiliac joint. The algorithm consists of four steps: (1) computation of an initial region of interest (ROI) that includes the pelvic joints region using heuristics and a U-Net classifier; (2) refinement of the ROI to detect both sacroiliiac joints using a four-tree random forest; (3) individual sacroiliitis grading of each sacroiliiac joint in each CT slice with a custom slice CNN classifier, and; (4) sacroiliitis diagnosis and grading by combining the individual slice grades using a random forest. Experimental results on 484 sacroiliiac joints yield a binary and a 3-class case classification accuracy of 91.9% and 86%, a sensitivity of 95% and 82%, and an Area-Under-the-Curve of 0.97 and 0.57, respectively. Automatic computer-based analysis of CT scans has the potential of being a useful method for the diagnosis and grading of sacroiliitis as an incidental finding.

Classification of contrast-enhanced spectral mammography (CESM) images.

PURPOSE: Contrast-enhanced spectral mammography (CESM) is a recently developed breast imaging technique. CESM relies on dual-energy acquisition following contrast agent injection to improve mammography sensitivity. CESM is comparable to contrast-enhanced MRI in terms of sensitivity, at a fraction of the cost. However, since lesion variability is large, even with the improved visibility provided by CESM, differentiation between benign and malignant enhancement is not accurate and a biopsy is usually performed for final assessment. Breast biopsies can be stressful to the patient and are expensive to healthcare systems. Moreover, as the biopsies results are most of the time benign, a specificity improvement in the radiologist diagnosis is required. This work presents a deep learning-based decision support system, which aims at improving the specificity of breast cancer diagnosis by CESM without affecting sensitivity. METHODS: We compare two analysis approaches, fine-tuning a pretrained network and fully training a convolutional neural network, for classification of CESM breast mass as benign or malignant. Breast Imaging Reporting and Data Systems (BIRADS) is a radiological lexicon, used with breast images, to categorize lesions. We improve each classification network by incorporating BIRADS textual features as an additional input to the network. We evaluate two ways of BIRADS fusion as network input: feature fusion and decision fusion. This leads to multimodal network architectures. At classification, we also exploit information from apparently normal breast tissue in the CESM of the considered patient, leading to a patient-specific classification. RESULTS: We evaluate performance using fivefold cross-validation, on 129 randomly selected breast lesions annotated by an experienced radiologist. Each annotation includes a contour of the mass in the image, biopsy-proven label of benign or malignant lesion and BIRADS descriptors. At 100% sensitivity, specificity of 66% was achieved using a multimodal network, which combines inputs at feature level and patient-specific classification. CONCLUSIONS: The presented multimodal network may significantly reduce benign biopsies, without compromising sensitivity.

3-D Neural denoising for low-dose Coronary CT Angiography (CCTA).

CCTA has become an important tool for coronary arteries assessment in low and medium risk patients. However, it exposes the patient to significant radiation doses, resulting from high image quality requirements and acquisitions at multiple cardiac phases. For widespread use of CCTA for coronary assessment, significant reduction of radiation exposure with minimal image quality loss is still needed. A neural denoising scheme, relying on a fully convolutional neural network (FCNN) architecture, is developed and applied to noisy CCTA. In contrast to previously published methods, the proposed FCNN is trained directly on 3-D CT data patches (blocks), implementing 3-D convolutions. Considering that anatomy is inherently tridimensional, the proposed 3-D approach may better capture and enforce inter-slice continuity of tiny structures. While training is performed on individual blocks, whole input scans can be fed and denoised in one piece, thus leveraging the fully convolutional architecture to maximize processing speed. The proposed method is compared to state-of-the-art denoising algorithms on a dataset of 45 CCTA scans. Low-dose scans are simulated by synthetic Poisson noise applied to the sinogram corresponding to a 90% reduction in radiation dose. The average feature similarity score (0.864) and the peak signal-to-noise ratio (41.47) obtained for the proposed algorithm outperformed the compared methods while requiring significantly shorter processing time. A set of 2-D FCNNs, structurally similar to the proposed 3-D network, are also implemented to demonstrate contribution of the additional dimension to the improved denoising. For further validation of the method coronary reconstruction using the Intellispace cardiac tool (Philips, Holland) is performed both on a real noisy CCTA scan and on the denoised scan using the proposed method. It is shown that the cardiac tool succeeds in reconstructing more coronaries using the scan denoised by the proposed method. The obtained results suggest the proposed method provides an efficient and powerful approach to low-dose CCTA denoising.

Fully automatic detection of renal cysts in abdominal CT scans.

PURPOSE: Simple renal cysts are a common benign finding in abdominal CT scans. However, since they may evolve in time, simple cysts need to be reported. With an ever-growing number of slices per CT scan, cysts are easily overlooked by the overloaded radiologist. In this paper, we address the detection of simple renal cysts as an incidental finding in a real clinical setting. METHODS: We propose a fully automatic framework for renal cyst detection, supported by a robust segmentation of the kidneys performed by a fully convolutional neural network. A combined 3D distance map of the kidneys and surrounding fluids provides initial candidates for cysts. Eventually, a second convolutional neural network classifies the candidates as cysts or non-cyst objects. RESULTS: Performance was evaluated on 52 abdominal CT scans selected at random in a real radiological workflow and containing over 70 cysts annotated by an experienced radiologist. Setting the minimal cyst diameter to 10 mm, the algorithm detected 59/70 cysts (true-positive rate = 84.3%) while producing an average of 1.6 false-positive per case. CONCLUSIONS: The obtained results suggest the proposed framework is a promising approach for the automatic detection of renal cysts as incidental findings of abdominal CT scans.

Patient-specific image denoising for ultra-low-dose CT-guided lung biopsies.

PURPOSE: Low-dose CT screening of the lungs is becoming a reality, triggering many more CT-guided lung biopsies. During these biopsies, the patient is submitted to repeated guiding scans with substantial cumulated radiation dose. Extension of the dose reduction to the biopsy procedure is therefore necessary. We propose an image denoising algorithm that specifically addresses the setup of CT-guided lung biopsies. It minimizes radiation exposure while keeping the image quality appropriate for navigation to the target lesion. METHODS: A database of high-SNR CT patches is used to filter noisy pixels in a non-local means framework, while explicitly enforcing local spatial consistency in order to preserve fine image details and structures. The patch database may be created from a multi-patient set of high-SNR lung scans. Alternatively, the first scan, acquired at high-SNR right before the needle insertion, can provide a convenient patient-specific patch database. RESULTS: The proposed algorithm is compared to state-of-the-art denoising algorithms for a dataset of 43 real CT-guided biopsy scans. Ultra-low-dose scans were simulated by synthetic noise addition to the sinogram, equivalent to a 96% reduction in radiation dose. The feature similarity score for the proposed algorithm outperformed the compared methods for all the scans in the dataset. The benefit of the patient-specific patch database over the multi-patient one is demonstrated in terms of recovered contrast for a tiny porcine lung nodule, following denoising with both approaches. CONCLUSIONS: The proposed method provides a promising approach to the denoising of ultra-low-dose CT-guided biopsy images.

Deformable registration of trans-rectal ultrasound (TRUS) and magnetic resonance imaging (MRI) for focal prostate brachytherapy.

PURPOSE: Focal therapy in low-risk prostate cancer may provide the best balance between cancer control and quality of life preservation. As a minimally invasive approach performed under TRUS guidance, brachytherapy is an appealing framework for focal therapy. However, the contrast in TRUS images is generally insufficient to distinguish the target lesion from normal prostate tissue. MRI usually offers a much better contrast between the lesion and surrounding tissues. Registration between TRUS and MRI may therefore significantly improve lesion targeting capability in focal prostate brachytherapy. In this paper, we present a deformable registration framework for the accurate fusion of TRUS and MRI prostate volumes under large deformations arising from dissimilarities in diameter, shape and orientation between endorectal coils and TRUS probes. METHODS: Following pose correction by a RANSAC implementation of the ICP algorithm, TRUS and MRI Prostate contour points are represented by a 3D extension of the shape-context descriptor and matched by the Hungarian algorithm. Eventually, a smooth free-form warping is computed by fitting a 3D B-spline mesh to the set of matched points. RESULTS: Quantitative validation of the registration accuracy is provided on a retrospective set of ten real cases, using as landmarks either brachytherapy seeds (six cases) or external beam radiotherapy fiducials (four cases) implanted and visible in both modalities. The average registration error between the landmarks was 2.49 and 3.20 mm, for the brachytherapy and external beam sets, respectively, that is less than the MRI voxels' long axis length ([Formula: see text]). The overall average registration error (for brachytherapy and external beam datasets together) was 2.56 mm. CONCLUSIONS: The proposed method provides a promising framework for TRUS-MRI registration in focal prostate brachytherapy.

Automatic classification of cancer cells in multispectral microscopic images of lymph node samples.

Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques. The method is based on the analysis of the spectral information of the cells as well as their morphological properties. A large number of descriptors is extracted for each cell location, which are used to train a supervised classifier which discriminates between normal and cancer cells. We show that a reliable analysis can be made with detection rate (recall) of 81%-100% for the cancer class.

A functional test for the detection of infusion lines extravasation.

Extravasation during intravenous (IV) infusion is a common secondary effect with potentially serious clinical consequences. The correct positioning of the needle in the vein may be difficult to confirm when no blood return is observed. In this paper, a novel method is proposed for the detection of extravasation during infusion therapy. A small volume of a sodium bicarbonate solution is administrated IV and, following its consecutive dissociation, an excess of carbon dioxide (CO2) is rapidly exhaled by the lungs. The analysis of the exhaled CO2 signal by a pattern recognition algorithm enables the robust detection of the CO2 excess release, thereby confirming the absence of extravasation. Initial results are presented for the application of the method on a group of 89 oncology patients.

From research to clinical practice: implementation of functional magnetic imaging and white matter tractography in the clinical environment.

In the last two decades functional magnetic resonance imaging (fMRI) has dominated research in neuroscience. However, only recently has it taken the first steps in translation to the clinical field. In this paper we describe the advantages of fMRI and DTI and the possible benefits of implementing these methods in clinical practice. We review the current clinical usages of fMRI and DTI and discuss the challenges and difficulties of translating these methods to clinical use. The most common application today is in neurosurgery. fMRI and DTI are done preoperatively for brain tumor patients who are having tumors removed and for epilepsy patients who are candidates for temporal resection. Imaging results supply the neurosurgeon with essential information regarding possible functional damage and thereby aid both in planning and performing surgery. Scientific research suggests more promising potential implementations of fMRI and DTI in improving diagnosis and rehabilitation. These advanced imaging methods can be used for pre-symptomatic diagnosis, as a differentiating biomarker in the absence of anatomical measurements, and for identification of mental response in the absence of motor-sensory abilities. These methods can aid and direct rehabilitation by predicting the success of possible interventions and rehabilitation options and by supplying a measure for biofeedback. This review opens a window to the state of the art neuroimaging methods being implemented these days into the clinical practice and provides a glance to the future clinical possibilities of fMRI and DTI.

A supervised framework for the registration and segmentation of white matter fiber tracts.

A supervised framework is presented for the automatic registration and segmentation of white matter (WM) tractographies extracted from brain DT-MRI. The framework relies on the direct registration between the fibers, without requiring any intensity-based registration as preprocessing. An affine transform is recovered together with a set of segmented fibers. A recently introduced probabilistic boosting tree classifier is used in a segmentation refinement step to improve the precision of the target tract segmentation. The proposed method compares favorably with a state-of-the-art intensity-based algorithm for affine registration of DTI tractographies. Segmentation results for 12 major WM tracts are demonstrated. Quantitative results are also provided for the segmentation of a particularly difficult case, the optic radiation tract. An average precision of 80% and recall of 55% were obtained for the optimal configuration of the presented method.

Co-registration of white matter tractographies by adaptive-mean-shift and Gaussian mixture modeling.

In this paper, we present a robust approach to the registration of white matter tractographies extracted from diffusion tensor-magnetic resonance imaging scans. The fibers are projected into a high dimensional feature space based on the sequence of their 3-D coordinates. Adaptive mean-shift clustering is applied to extract a compact set of representative fiber-modes (FM). Each FM is assigned to a multivariate Gaussian distribution according to its population thereby leading to a Gaussian mixture model (GMM) representation for the entire set of fibers. The registration between two fiber sets is treated as the alignment of two GMMs and is performed by maximizing their correlation ratio. A nine-parameters affine transform is recovered and eventually refined to a twelve-parameters affine transform using an innovative mean-shift based registration refinement scheme presented in this paper. The validation of the algorithm on synthetic intrasubject data demonstrates its robustness to interrupted and deviating fiber artifacts as well as outliers. Using real intrasubject data, a comparison is conducted to other intensity based and fiber-based registration algorithms, demonstrating competitive results. An option for tracking-in-time, on specific white matter fiber tracts, is also demonstrated on the real data.

An adaptive mean-shift framework for MRI brain segmentation.

An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels into one of three main tissue types: gray matter, white matter, and Cerebro-spinal fluid. The MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features. An adaptive mean-shift algorithm clusters the joint spatial-intensity feature space, thus extracting a representative set of high-density points within the feature space, otherwise known as modes. Tissue segmentation is obtained by a follow-up phase of intensity-based mode clustering into the three tissue categories. By its nonparametric nature, adaptive mean-shift can deal successfully with nonconvex clusters and produce convergence modes that are better candidates for intensity based classification than the initial voxels. The proposed method is validated on 3-D single and multimodal datasets, for both simulated and real MRI data. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas.

Probabilistic space-time video modeling via piecewise GMM.

In this paper, we describe a statistical video representation and modeling scheme. Video representation schemes are needed to segment a video stream into meaningful video-objects, useful for later indexing and retrieval applications. In the proposed methodology, unsupervised clustering via Gaussian mixture modeling extracts coherent space-time regions in feature space, and corresponding coherent segments (video-regions) in the video content. A key feature of the system is the analysis of video input as a single entity as opposed to a sequence of separate frames. Space and time are treated uniformly. The probabilistic space-time video representation scheme is extended to a piecewise GMM framework in which a succession of GMMs are extracted for the video sequence, instead of a single global model for the entire sequence. The piecewise GMM framework allows for the analysis of extended video sequences and the description of nonlinear, nonconvex motion patterns. The extracted space-time regions allow for the detection and recognition of video events. Results of segmenting video content into static versus dynamic video regions and video content editing are presented.