Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method

Purpose

Patient-specific quantitative assessments of muscle mass and biomechanical musculoskeletal simulations require segmentation of the muscles from medical images. The objective of this work is to automate muscle segmentation from CT data of the hip and thigh.

Method

We propose a hierarchical multi-atlas method in which each hierarchy includes spatial normalization using simpler pre-segmented structures in order to reduce the inter-patient variability of more complex target structures.

Results

The proposed hierarchical method was evaluated with 19 muscles from 20 CT images of the hip and thigh using the manual segmentation by expert orthopedic surgeons as ground truth. The average symmetric surface distance was significantly reduced in the proposed method (1.53 mm) in comparison with the conventional method (2.65 mm).

Conclusion

We demonstrated that the proposed hierarchical multi-atlas method improved the accuracy of muscle segmentation from CT images, in which large inter-patient variability and insufficient contrast were involved.

     

The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes

Purpose

Existing methods for sorting, labeling, registering, and across-subject localization of electrodes in intracranial encephalography (iEEG) may involve laborious work requiring manual inspection of radiological images.

Methods

We describe a new open-source software package, the interactive electrode localization utility which presents a full pipeline for the registration, localization, and labeling of iEEG electrodes from CT and MR images. In addition, we describe a method to automatically sort and label electrodes from subdural grids of known geometry.

Results

We validated our software against manual inspection methods in twelve subjects undergoing iEEG for medically intractable epilepsy. Our algorithm for sorting and labeling performed correct identification on 96% of the electrodes.

Conclusions

The sorting and labeling methods we describe offer nearly perfect performance and the software package we have distributed may simplify the process of registering, sorting, labeling, and localizing subdural iEEG grid electrodes by manual inspection.

     

Development of digital phantoms based on a finite element model to simulate low-attenuation areas in CT imaging for pulmonary emphysema quantification

Purpose

To develop an innovative finite element (FE) model of lung parenchyma which simulates pulmonary emphysema on CT imaging. The model is aimed to generate a set of digital phantoms of low-attenuation areas (LAA) images with different grades of emphysema severity.

Methods

Four individual parameter configurations simulating different grades of emphysema severity were utilized to generate 40 FE models using ten randomizations for each setting. We compared two measures of emphysema severity (relative area (RA) and the exponent D of the cumulative distribution function of LAA clusters size) between the simulated LAA images and those computed directly on the models output (considered as reference).

Results

The LAA images obtained from our model output can simulate CT-LAA images in subjects with different grades of emphysema severity. Both RA and D computed on simulated LAA images were underestimated as compared to those calculated on the models output, suggesting that measurements in CT imaging may not be accurate in the assessment of real emphysema extent.

Conclusions

Our model is able to mimic the cluster size distribution of LAA on CT imaging of subjects with pulmonary emphysema. The model could be useful to generate standard test images and to design physical phantoms of LAA images for the assessment of the accuracy of indexes for the radiologic quantitation of emphysema.

     

A novel platform for electromagnetic navigated ultrasound bronchoscopy (EBUS)

Purpose

Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) of mediastinal lymph nodes is essential for lung cancer staging and distinction between curative and palliative treatment. Precise sampling is crucial. Navigation and multimodal imaging may improve the efficiency of EBUS-TBNA. We demonstrate a novel EBUS-TBNA navigation system in a dedicated airway phantom.

Methods

Using a convex probe EBUS bronchoscope (CP-EBUS) with an integrated sensor for electromagnetic (EM) position tracking, we performed navigated CP-EBUS in a phantom. Preoperative computed tomography (CT) and real-time ultrasound (US) images were integrated into a navigation platform for EM navigated bronchoscopy. The coordinates of targets in CT and US volumes were registered in the navigation system, and the position deviation was calculated.

Results

The system visualized all tumor models and displayed their fused CT and US images in correct positions in the navigation system. Navigating the EBUS bronchoscope was fast and easy. Mean error observed between US and CT positions for 11 target lesions (37 measurements) was \(2.8\pm 1.0\) mm, maximum error was 5.9 mm.

Conclusion

The feasibility of our novel navigated CP-EBUS system was successfully demonstrated. An EBUS navigation system is needed to meet future requirements of precise mediastinal lymph node mapping, and provides new opportunities for procedure documentation in EBUS-TBNA.

     

Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications

Purpose

To develop and validate a fully automatic method for segmentation of paraspinal muscles from 3D torso CT images.

Methods

We propose a novel learning-based method to address this challenging problem. Multi-scale iterative random forest classifications with multi-source information are employed in this study to speed up the segmentation and to improve the accuracy. Here, multi-source images include the original torso CT images and later also the iteratively estimated and refined probability maps of the paraspinal muscles. We validated our method on 20 torso CT data with associated manual segmentation. We randomly partitioned the 20 CT data into two evenly distributed groups and took one group as the training data and the other group as the test data.

Results

The proposed method achieved a mean Dice coefficient of 93.0%. It took on average 46.5 s to segment a 3D torso CT image with the size ranging from \(512 \times 512 \times 802\) voxels to \(512 \times 512 \times 1031\) voxels.

Conclusions

Our fully automatic, learning-based method can accurately segment paraspinal muscles from 3D torso CT images. It generates segmentation results that are better than those achieved by the state-of-the-art methods.

     

A general method for cupping artifact correction of cone-beam breast computed tomography images

Purpose

Cone-beam breast computed tomography (CBBCT), a promising breast cancer diagnostic technique, has been under investigation for the past decade. However, owing to scattered radiation and beam hardening, CT numbers are not uniform on CBBCT images. This is known as cupping artifact, and it presents an obstacle for threshold-based volume segmentation. In this study, we proposed a general post-reconstruction method for cupping artifact correction.

Methods

There were four steps in the proposed method. First, three types of local region histogram peaks were calculated: adipose peaks with low CT numbers, glandular peaks with high CT numbers, and unidentified peaks. Second, a linear discriminant analysis classifier, which was trained by identified adipose and glandular peaks, was employed to identify the unidentified peaks as adipose or glandular peaks. Third, adipose background signal profile was fitted according to the adipose peaks using the least squares method. Finally, the adipose background signal profile was subtracted from original image to obtain cupping corrected image

Results

In experimental study, standard deviation of adipose tissue CT numbers was obviously reduced and the CT numbers were more uniform after cupping correction by proposed method; in simulation study, root-mean-square errors were significantly reduced for both symmetric and asymmetric cupping artifacts, indicating that the proposed method was effective to both artifacts.

Conclusions

A general method without a circularly symmetric assumption was proposed to correct cupping artifacts in CBBCT images for breast. It may be properly applied to images of real patient breasts with natural pendent geometry.

     

Comparison of conventional ultrasonography and ultrasonography-computed tomography fusion imaging for target identification using digital/real hybrid phantoms: a preliminary study

Purpose

This preliminary study compared ultrasonography-computed tomography (US-CT) fusion imaging and conventional ultrasonography (US) for accuracy and time required for target identification using a combination of real phantoms and sets of digitally modified computed tomography (CT) images (digital/real hybrid phantoms).

Methods

In this randomized prospective study, 27 spheres visible on B-mode US were placed at depths of 3.5, 8.5, and 13.5 cm (nine spheres each). All 27 spheres were digitally erased from the CT images, and a radiopaque sphere was digitally placed at each of the 27 locations to create 27 different sets of CT images. Twenty clinicians were instructed to identify the sphere target using US alone and fusion imaging. The accuracy of target identification of the two methods was compared using McNemar’s test. The mean time required for target identification and error distances were compared using paired t tests.

Results

At all three depths, target identification was more accurate and the mean time required for target identification was significantly less with US-CT fusion imaging than with US alone, and the mean error distances were also shorter with US-CT fusion imaging.

Conclusion

US-CT fusion imaging was superior to US alone in terms of accurate and rapid identification of target lesions.

     

Regional infarction identification from cardiac CT images: a computer-aided biomechanical approach

Purpose

Regional infarction identification is important for heart disease diagnosis and management, and myocardial deformation has been shown to be effective for this purpose. Although tagged and strain-encoded MR images can provide such measurements, they are uncommon in clinical routine. On the contrary, cardiac CT images are more available with lower costs, but they only provide motion of cardiac boundaries and additional constraints are required to obtain the myocardial strains. The goal of this study is to verify the potential of contrast-enhanced CT images on computer-aided regional infarction identification.

Methods

We propose a biomechanical approach combined with machine learning algorithms. A hyperelastic biomechanical model is used with deformable image registration to estimate 3D myocardial strains from CT images. The regional strains and CT image intensities are input to a classifier for regional infarction identification. Cross-validations on ten canine image sequences with artificially induced infarctions were used to study the performances of using different feature combinations and machine learning algorithms.

Results

Radial strain, circumferential strain, first principal strain, and image intensity were shown to be discriminative features. The highest identification accuracy (\(85\pm 14\) %) was achieved when combining radial strain with image intensity. Random forests gave better results than support vector machines on less discriminative features. Random forests also performed better when all strains were used together.

Conclusion

Although CT images cannot directly measure myocardial deformation, with the use of a biomechanical model, the estimated strains can provide promising identification results especially when combined with CT image intensity.

     

Single STE-MR Acquisition in MR-Based Attenuation Correction of Brain PET Imaging Employing a Fully Automated and Reproducible Level-Set Segmentation Approach

Purpose

The aim of this study is to introduce a fully automatic and reproducible short echo-time (STE) magnetic resonance imaging (MRI) segmentation approach for MR-based attenuation correction of positron emission tomography (PET) data in head region.

Procedures

Single STE-MR imaging was followed by generating attenuation correction maps (μ-maps) through exploiting an automated clustering-based level-set segmentation approach to classify head images into three regions of cortical bone, air, and soft tissue. Quantitative assessment was performed by comparing the STE-derived region classes with the corresponding regions extracted from X-ray computed tomography (CT) images.

Results

The proposed segmentation method returned accuracy and specificity values of over 90 % for cortical bone, air, and soft tissue regions. The MR- and CT-derived μ-maps were compared by quantitative histogram analysis.

Conclusions

The results suggest that the proposed automated segmentation approach can reliably discriminate bony structures from the proximal air and soft tissue in single STE-MR images, which is suitable for generating MR-based μ-maps for attenuation correction of PET data.

     

Shading correction for endoscopic images using principal color components

Purpose

Inhomogeneous illumination often causes significant shading and vignetting effects in images captured by an endoscope. Most of the established shading correction methods are designed for gray-level images. Only few papers have been published about how to compensate for shading in color images. For endoscopic images with a distinct red coloring, these methods tend to produce color artifacts.

Method

A color shading correction algorithm for endoscopic images is proposed. Principal component analysis is used to calculate an appropriate estimate of the shading effect so that a one-channel shading correction can be applied without producing undesired artifacts.

Results

The proposed method is compared to established YUV and HSV color-conversion-based approaches. It produces superior results both on simulated and on real endoscopic images. Example images of using the proposed shading correction for endoscopic image mosaicking are presented.

Conclusion

A new method for shading correction is presented which is tailored to images with distinct coloring. It is beneficial for the visual impression and further image analysis tasks.

     

Isolated recto-sigmoid colitis: a new imaging pattern of ipilimumab-associated colitis

Purpose

The purpose of this study is to describe typical CT findings and distinct imaging patterns of ipilimumab-associated colitis in immunotherapeutic treatment of melanoma.

Materials and methods

This HIPAA-compliant retrospective study included 86 patients with melanoma imaged with CT or PET/CT of the abdomen and pelvis during or shortly after administration of ipilimumab. Twelve of 86 patients (14%) developed symptoms of colitis and underwent CT imaging of the abdomen and pelvis while symptomatic. Two radiologists reviewed CT images to evaluate for the presence of CT findings of colitis including mesenteric vessel engorgement, pericolonic inflammatory change, hyperenhancement of colonic mucosa, colonic wall thickening, fluid-filled colonic distension, pneumoperitoneum, pneumatosis, and diverticulosis in the inflamed segment of colon. One nuclear medicine radiologist reviewed PET images for abnormally increased FDG uptake in the colon. The diagnosis of ipilimumab-associated colitis was made based on clinical presentation, imaging findings, and laboratory data.

Results

Common CT findings of ipilimumab-associated colitis included colonic mucosal hyperenhancement (10/12 [83%]), mesenteric vessel engorgement (9/12 [75.0%]), colonic wall thickening (9/12 [75%]), and pericolonic fat stranding (2/12 [16%]). No patient developed pneumatosis or pneumoperitoneum. Diffuse colitis was present in 4/12 (33%) patients. Segmental colitis with associated diverticulosis (was present in 2/12 (17%) patients). A third pattern, isolated recto-sigmoid colitis without diverticulosis, was observed in 6/12 (50%) patients. All patients with colitis demonstrated recto-sigmoid involvement.

Conclusions

A third radiologic pattern of ipilimumab-associated colitis was observed in this study: isolated recto-sigmoid colitis without diverticulosis. All patterns of ipilimumab-associated colitis include recto-sigmoid involvement.

     

Automated pulmonary nodule CT image characterization in lung cancer screening

Purpose

In lung cancer screening, pulmonary nodules are first identified in low-dose chest CT images. Costly follow-up procedures could be avoided if it were possible to establish the malignancy status of these nodules from these initial images. Preliminary computer methods have been proposed to characterize the malignancy status of pulmonary nodules based on features extracted from a CT image. The parameters and performance of such a computer system in a lung cancer screening context are addressed.

Methods

A computer system that incorporates novel 3D image features to determine the malignancy status of pulmonary nodules is evaluated with a large dataset constructed from images from the NLST and ELCAP lung cancer studies. The system is evaluated with different data subsets to determine the impact of class size distribution imbalance in datasets and to evaluate different training and testing strategies.

Results

Results show a modest improvement in malignancy prediction compared to prediction by size alone for a traditional size-unbalanced dataset. Further, the advantage of size binning for classifier design and the advantages of a size-balanced dataset for both training and testing are demonstrated.

Conclusion

Nodule classification in the context of low-resolution low-dose whole-chest CT images for the clinically relevant size range in the context of lung cancer screening is highly challenging, and results are moderate compared to what has been reported in the literature for other clinical contexts. Nodule class size distribution imbalance needs to be considered in the training and evaluation of computer-aided diagnostic systems for producing patient-relevant outcomes.

     

Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [<Superscript>18</Superscript>F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation

Purpose

To assess (1) the repeatability and (2) the impact of reconstruction methods and delineation on the repeatability of 105 radiomic features in non-small-cell lung cancer (NSCLC) 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomorgraphy/computed tomography (PET/CT) studies.

Procedures

Eleven NSCLC patients received two baseline whole-body PET/CT scans. Each scan was reconstructed twice, once using the point spread function (PSF) and once complying with the European Association for Nuclear Medicine (EANM) guidelines for tumor PET imaging. Volumes of interest (n?=?19) were delineated twice, once on PET and once on CT images.

Results

Sixty-three features showed an intraclass correlation coefficient?≥?0.90 independent of delineation or reconstruction. More features were sensitive to a change in delineation than to a change in reconstruction (25 and 3 features, respectively).

Conclusions

The majority of features in NSCLC [¹⁸F]FDG-PET/CT studies show a high level of repeatability that is similar or better compared to simple standardized uptake value measures.

     

Adaptive noise correction of dual-energy computed tomography images

Purpose

Noise reduction in material density images is a necessary preprocessing step for the correct interpretation of dual-energy computed tomography (DECT) images. In this paper we describe a new method based on a local adaptive processing to reduce noise in DECT images

Methods

An adaptive neighborhood Wiener (ANW) filter was implemented and customized to use local characteristics of material density images. The ANW filter employs a three-level wavelet approach, combined with the application of an anisotropic diffusion filter. Material density images and virtual monochromatic images are noise corrected with two resulting noise maps.

Results

The algorithm was applied and quantitatively evaluated in a set of 36 images. From that set of images, three are shown here, and nine more are shown in the online supplementary material. Processed images had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than the raw material density images. The average improvements in SNR and CNR for the material density images were 56.5 and 54.75 %, respectively.

Conclusion

We developed a new DECT noise reduction algorithm. We demonstrate throughout a series of quantitative analyses that the algorithm improves the quality of material density images and virtual monochromatic images.

     

Intra-operative adjustment of standard planes in C-arm CT image data

Purpose

With the help of an intra-operative mobile C-arm CT, medical interventions can be verified and corrected, avoiding the need for a post-operative CT and a second intervention. An exact adjustment of standard plane positions is necessary for the best possible assessment of the anatomical regions of interest but the mobility of the C-arm causes the need for a time-consuming manual adjustment. In this article, we present an automatic plane adjustment at the example of calcaneal fractures.

Methods

We developed two feature detection methods (2D and pseudo-3D) based on SURF key points and also transferred the SURF approach to 3D. Combined with an atlas-based registration, our algorithm adjusts the standard planes of the calcaneal C-arm images automatically. The robustness of the algorithms is evaluated using a clinical data set. Additionally, we tested the algorithm’s performance for two registration approaches, two resolutions of C-arm images and two methods for metal artifact reduction.

Results

For the feature extraction, the novel 3D-SURF approach performs best. As expected, a higher resolution (\(512^3\) voxel) leads also to more robust feature points and is therefore slightly better than the \(256^3\) voxel images (standard setting of device). Our comparison of two different artifact reduction methods and the complete removal of metal in the images shows that our approach is highly robust against artifacts and the number and position of metal implants.

Conclusions

By introducing our fast algorithmic processing pipeline, we developed the first steps for a fully automatic assistance system for the assessment of C-arm CT images.

     

Olanzapine vs haloperidol: treating delirium in a critical care setting

Objective

To compare the safety and estimate the response profile of olanzapine, a second-generation antipsychotic, to haloperidol in the treatment of delirium in the critical care setting.

Design

Prospective randomized trial

Setting

Tertiary care university affiliated critical care unit.

Patients

All admissions to a medical and surgical intensive care unit with a diagnosis of delirium.

Interventions

Patients were randomized to receive either enteral olanzapine or haloperidol.

Measurements

Patient’s delirium severity and benzodiazepine use were monitored over 5 days after the diagnosis of delirium.

Main results

Delirium Index decreased over time in both groups, as did the administered dose of benzodiazepines. Clinical improvement was similar in both treatment arms. No side effects were noted in the olanzapine group, whereas the use of haloperidol was associated with extrapyramidal side effects.

Conclusions

Olanzapine is a safe alternative to haloperidol in delirious critical care patients, and may be of particular interest in patients in whom haloperidol is contraindicated.

     

Laser-assisted flat-detector CT-guided intracranial access

Purpose

Flat-detector CT can be integrated with C-arm fluoroscopy for CT-guided neurosurgical and endovascular procedures. We studied the accuracy of this technique with laser assistance in targeting intracranial lesions in a cranial model.

Methods

An acrylic scale-model skull containing foam parenchyma was embedded with 2.16-mm-diameter targets. A flat-detector CT was acquired and registered to the skull’s position. Ten targets were accessed with biopsy needles under fluoroscopic guidance, flat-detector CT overlay, and laser assistance. Accuracy was measured from the needle tip to the target center using flat-detector CT.

Results

Ten targets were accessed successfully using XperGuide software. Needles were placed within 1.30 \(\pm \) 0.63 mm of target isocenter. Accuracy did not vary by entry site, operator, location, or lesion depth.

Conclusions

Laser-assisted flat-detector CT-guided targeting of all intracranial targets was successful with excellent accuracy. This technique can be applied to other minimally invasive neurosurgical procedures.

     

Early nontumorous CT findings after irreversible electroporation of locally advanced pancreatic cancer

Purpose

The use of irreversible electroporation (IRE) has been a relatively recent development in the palliative treatment of locally advanced pancreatic cancer. With CT as a key modality in patient follow-up, recognition of nontumorous imaging findings is paramount after IRE.

Methods

A retrospective review of patients having undergone IRE for locally advanced pancreatic adenocarcinoma was performed. A total of 36 patients met inclusion criteria and their imaging studies were reviewed by two radiologists. Nontumorous abnormalities identified in the peri-electroporation bed on Computed Tomography (CT) during the early postoperative period (within 30 days) were characterized and classified into categories.

Results

Our results indicate that the most common nontumorous findings in the peri-electroporation bed were vascular, followed by changes involving the gastrointestinal tract, peritoneal cavity, and, infrequently, the biliary tree.

Conclusions

Interpretation of CT imaging of the postoperative peri-electroporation bed is challenging. This review of CT findings allows the radiologist to recognize and anticipate significant nontumorous findings in the peri-electroporation bed during early follow-up after IRE.

     

Intra-operative assessment of fractured articular surfaces in cone beam CT image data

Purpose

The assessment of intra-operatively acquired volumetric data is a difficult and often time-consuming task, which demands a new set of skills from the surgeons. In the case of orthopedic surgeries such as the treatment of calcaneal fractures, the correctness of the reduction of the bone fragments can be verified with the help of C-arm CT volumetric images. For an accurate intra-operative assessment of the displaced fragments, an automatic segmentation of the articular surfaces and color-coded visualization was developed.

Methods

Our automatic approach consists of three major steps: first, using adjusted standard planes intersecting the articular region, the joint space is localized with an intensity profile-based method. In a second step, the localized joint space is segmented on the Laplacian of Gaussian filtered volumetric image by a modified binary flood fill algorithm. Finally, a 3D surface model of the segmented joint space is analyzed and visualized with focus on critical displacements of the surface.

Results

A specifically designed human cadaver study consisting of ten lower legs of ten different donors was conducted to acquire 48 realistic C-arm CT images of misaligned bone fragments (steps of varying sizes) in the posterior talar articular surface of the calcaneus. The proposed algorithmic pipeline was verified by the acquired image data and showed very good results with no false positives and an overall correct displacement assessment of \(93.8\,\%\).

Conclusions

The proposed algorithmic pipeline can be easily integrated into the clinical workflow and qualifies for intra-operative usage. It showed very good results on the reference data set of the cadaver study. With the help of such an assistance system, the time-consuming process of 2D view adjustment and visual assessment of the gray value images can be greatly simplified.