Multislice CT angiography in post-aortic stent grafting: optimization of scanning protocols for virtual intravascular endoscopy

Objective

The aim of this study is to investigate the optimal 64-slice CT scanning protocols of 3D virtual intravascular endoscopy (VIE) visualization in abdominal aortic aneurysm treated with suprarenal stent grafts, based on an in vitro phantom study.

Materials and methods

The study was performed on a human aorta phantom with a commercially available stent graft in situ. The contrast medium was diluted to produce CT attenuation similar to that used in routine abdominal aortic CT angiography. A series of scans was performed on a 64-slice CT scanner with the scanning protocols being section thickness of 0.5, 1.0, 2.0, 3.0 and 5.0 mm, pitch of 0.9, 1.2 and 1.4 with reconstruction interval of 50% overlap. Quantitative assessment of image quality was performed by measuring the standard deviation (SD) on surfaced rendered VIE images at three anatomic locations, superior mesenteric artery, right renal artery and aortic aneurysm. This aims to determine the degree of stair-step artifacts present on VIE images using a line profile. The thickness of suprarenal stent wires was measured corresponding with each scanning protocol at above same three locations. Subjective assessment of image quality was focused to evaluate the configuration of aortic ostium visualized on VIE images.

Results

Our results showed that the SD was independent of section thickness and pitch value, although thinner section thickness of 0.5 and 1.0 mm produced better image quality with fewer artifacts. The aortic ostium became irregular or distorted when the section thickness increased to 3.0 and 5.0 mm. Radiation dose was inversely proportional to the pitch values.

Conclusion

We recommend a scanning protocol of 1.0 mm and pitch 1.4 with reconstruction interval of 0.5 mm as the optimal one of VIE in post-aortic stent grafting as it allows for generation of acceptable images, with fewer artifacts and less radiation dose.     

Local noise estimation in low-dose chest CT images

Purpose

   Image noise in computed tomography (CT) images may have significant local variation due to tissue properties, dose, and location of the X-ray source. We developed and tested an automated tissue-based estimator method for estimating local noise in CT images.

Method

   An automated TBE method for estimating the local noise in CT image in 3 steps was developed: (1) Partition the image into homogeneous and transition regions, (2) For each pixel in the homogeneous regions, compute the standard deviation in a $15\times 15\times 1$ voxel local region using only pixels from the same homogeneous region, and (3) Interpolate the noise estimate from the homogeneous regions in the transition regions. Noise-aware fat segmentation was implemented. Experiments were conducted on the anthropomorphic phantom and in vivo low-dose chest CT scans to validate the TBE, characterize the magnitude of local noise variation, and determine the sensitivity of noise estimates to the size of the region in which noise is computed. The TBE was tested on all scans from the Early Lung Cancer Action Program public database. The TBE was evaluated quantitatively on the phantom data and qualitatively on the in vivo data.

Results

   The results show that noise can vary locally by over 200 Hounsfield units on low-dose in vivo chest CT scans and that the TBE can characterize these noise variations within 5 %. The new fat segmentation algorithm successfully improved segmentation on all 50 scans tested.

Conclusion

   The TBE provides a means to estimate noise for image quality monitoring, optimization of denoising algorithms, and improvement of segmentation algorithms. The TBE was shown to accurately characterize the large local noise variations that occur due to changes in material, dose, and X-ray source location.     

Fully automatic segmentation of the Femur from 3D-CT images using primitive shape recognition and statistical shape models

Purpose

Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM.

Method

Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting.

Results

The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1 mm.

Conclusion

The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur.     

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.

     

Segmentation of radiographic images under topological constraints: application to the femur

Purpose

A framework for radiographic image segmentation under topological control based on two-dimensional (2D) image analysis was developed. The system is intended for use in common radiological tasks including fracture treatment analysis, osteoarthritis diagnostics and osteotomy management planning.

Methods

The segmentation framework utilizes a generic three-dimensional (3D) model of the bone of interest to define the anatomical topology. Non-rigid registration is performed between the projected contours of the generic 3D model and extracted edges of the X-ray image to achieve the segmentation. For fractured bones, the segmentation requires an additional step where a region-based active contours curve evolution is performed with a level set Mumford–Shah method to obtain the fracture surface edge. The application of the segmentation framework to analysis of human femur radiographs was evaluated. The proposed system has two major innovations. First, definition of the topological constraints does not require a statistical learning process, so the method is generally applicable to a variety of bony anatomy segmentation problems. Second, the methodology is able to handle both intact and fractured bone segmentation.

Results

Testing on clinical X-ray images yielded an average root mean squared distance (between the automatically segmented femur contour and the manual segmented ground truth) of 1.10 mm with a standard deviation of 0.13 mm. The proposed point correspondence estimation algorithm was benchmarked against three state-of-the-art point matching algorithms, demonstrating successful non-rigid registration for the cases of interest.

Conclusions

A topologically constrained automatic bone contour segmentation framework was developed and tested, providing robustness to noise, outliers, deformations and occlusions.     

Using a shape prior for robust modeling of the mitral annulus on 4D ultrasound data

Purpose

   Over 40,000 annuloplasty rings are implanted each year in the USA to treat mitral regurgitation. However, the used measuring techniques to select a suitable annuloplasty ring are imprecise and highly depending on the expert’s experience. This can cause a re-occurrence of the mitral regurgitation or an annuloplasty ring dehiscence, and thus the necessity of a re-operation. We propose a method to create a 4D model of the mitral annulus from ultrasound data to enable precise measurement and patient-specific implant planning.

Methods

   An initial mitral annulus model is placed interactively in the 4D image data by defining commissure points and the annulus plane for one time step in diastole and systole. The model is automatically optimized using distinct image features. A shape and pose prior of the mitral annulus is used to compensate for artifacts and to enforce a plausible anatomical morphology, while a temporal alignment ensures a natural motion of the 4D model.

Results

   Ground truth data were created for 4D images of 42 patients with varying image quality. A parameter and shape prior training was performed on a third of the ground truth data, while the rest was used to validate the method. The average error of the resulting mitral annulus models was computed as 2.25 ( \(\pm 0.38\) ) mm. The average expert standard deviation was determined as 1.86 ( \(\pm 0.32\) ) mm.

Conclusion

   The proposed method enables the 4D modeling of mitral annuli based on ultrasound data in less than 2 min. The resulting models are comparable to manually delineated models and can be used for measurements of annular geometries and patient-specific annuloplasty treatment planning.     

Preliminary assessment of cardiac short term safety and efficacy of manganese chloride for cardiovascular magnetic resonance in humans

Background

The risk of aortic dissection is 100-fold increased in Turner syndrome (TS). Unfortunately, risk stratification is inadequate due to a lack of insight into the natural course of the syndrome-associated aortopathy. Therefore, this study aimed to prospectively assess aortic dimensions in TS.

Methods

Eighty adult TS patients were examined twice with a mean follow-up of 2.4 ± 0.4 years, and 67 healthy age and gender-matched controls were examined once. Aortic dimensions were measured at nine predefined positions using 3D, non-contrast and free-breathing cardiovascular magnetic resonance. Transthoracic echocardiography and 24-hour ambulatory blood pressure were also performed.

Results

At baseline, aortic diameters (body surface area indexed) were larger at all positions in TS. Aortic dilation was more prevalent at all positions excluding the distal transverse aortic arch. Aortic diameter increased in the aortic sinus, at the sinotubular junction and in the mid-ascending aorta with growth rates of 0.1 - 0.4 mm/year. Aortic diameters at all other positions were unchanged. The bicuspid aortic valve conferred higher aortic sinus growth rates (p < 0.05). No other predictors of aortic growth were identified.

Conclusion

A general aortopathy is present in TS with enlargement of the ascending aorta, which is accelerated in the presence of a bicuspid aortic valve.     

Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets

Purpose

Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images.

Methods

The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model.

Results

Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency.

Conclusion

A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.

     

Fast and automatic bone segmentation and registration of 3D ultrasound to CT for the full pelvic anatomy: a comparative study

Purpose

Ultrasound (US) is a safer alternative to X-rays for bone imaging, and its popularity for orthopedic surgical navigation is growing. Routine use of intraoperative US for navigation requires fast, accurate and automatic alignment of tracked US to preoperative computed tomography (CT) patient models. Our group previously investigated image segmentation and registration to align untracked US to CT of only the partial pelvic anatomy. In this paper, we extend this to study the performance of these previously published techniques over the full pelvis in a tracked framework, to characterize their suitability in more realistic scenarios, along with an additional simplified segmentation method and similarity metric for registration.

Method

We evaluated phase symmetry segmentation, and Gaussian mixture model (GMM) and coherent point drift (CPD) registration methods on a pelvic phantom augmented with human soft tissue images. Additionally, we proposed and evaluated a simplified 3D bone segmentation algorithm we call Shadow–Peak (SP), which uses acoustic shadowing and peak intensities to detect bone surfaces. We paired this with a registration pipeline that optimizes the normalized cross-correlation (NCC) between distance maps of the segmented US–CT images.

Results

SP segmentation combined with the proposed NCC registration successfully aligned tracked US volumes to the preoperative CT model in all trials, in contrast to the other techniques. SP with NCC achieved a median target registration error (TRE) of 2.44 mm (maximum 4.06 mm), when imaging all three anterior pelvic structures, and a mean runtime of 27.3 s. SP segmentation with CPD registration was the next most accurate combination: median TRE of 3.19 mm (maximum 6.07 mm), though a much faster runtime of 4.2 s.

Conclusion

We demonstrate an accurate, automatic image processing pipeline for intraoperative alignment of US–CT over the full pelvis and compare its performance with the state-of-the-art methods. The proposed methods are amenable to clinical implementation due to their high accuracy on realistic data and acceptably low runtimes.

     

Intraoperative segmentation of iodine and palladium radioactive sources in C-arm images

Purpose

   Dynamic dosimetry is becoming the standard to evaluate the quality of radioactive implants during brachytherapy. For this, it is essential to obtain a 3D visualization of the implanted seeds and their relative position to the prostate. A method was developed to obtain a robust and precise segmentation of seeds in C-arm images, and this approach was tested using clinical datasets.

Method

   A region-based implicit active contour approach was used to delineate implanted seeds. Then, a template-based matching was employed to segment iodine implants whereas a K-means algorithm is implemented to resolve palladium seed clusters. To validate the method, 55 C-arm images from 10 patients were used for the segmentation of iodine sources, whereas 225 C-arm images from 16 patients were used for the palladium case.

Results

   Compared to manual ground truth segmentation of 6,002 iodine seeds and 15,354 palladium seeds, 98.7 % of iodine sources were automatically detected and declustered showing a false-positive rate of only 1.7 %. A total of 98.7 % of palladium sources were automatically detected and declustered with a false-positive rate of only 2.0 %.

Conclusion

   An automated segmentation method was developed that is able to perform the identification and annotation processes of seeds on par with a human expert. This method was shown to be robust and suitable for integration in the dynamic dosimetry workflow of prostate brachytherapy interventions.     

Normal main portal vein diameter measured on CT is larger than the widely referenced upper limit of 13 mm

Objective

We determined mean main portal vein diameter in healthy patients evaluated with CT, compared this value to the “upper limit of normal” reported previously, and evaluated effects of age, sex, height, and BMI on portal vein diameter.

Materials and methods

Our cohort of healthy patients underwent abdominal CT as potential renal donors. We excluded patients with evidence of liver or severe cardiac disease. We recorded patients’ age, sex, height, weight, and BMI. Patients’ main portal vein diameters were measured by fellowship-trained abdominal imagers on non-contrast and post-contrast images in axial and coronal projections at a defined location. A general linear mixed model was used for analysis.

Results

191 patients with 679 main portal vein measurements were included in the analysis. Mean main portal vein diameter was 15.5 ± 1.9 mm; this value was significantly different from the upper limit of normal of 13 mm commonly referenced in the literature (95% CI: 2.22–2.69 mm higher, p < 0.0001). Portal vein diameter does not vary significantly when measured on axial vs. coronal images. On average, post-contrast main portal veins were 0.56 mm larger compared to non-contrast, (95% CI: 0.40–0.71 mm, p < 0.0071). Patient height and BMI are positively correlated with MPV diameter.

Conclusions

Normal mean portal vein diameter measured on CT was significantly larger (mean 15.5 mm) than the accepted upper limit of 13 mm. Contrast-enhanced main portal veins are significantly larger (0.56 mm) than unenhanced. Sex, height, and BMI significantly affect main portal vein diameter.

     

Shape–intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images

Purpose

Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images.

Methods

First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically generate a subject-specific probabilistic atlas for the test image. The most likely liver region of the test image is further determined based on the generated atlas. A rough segmentation is obtained by a maximum a posteriori classification of probability map, and the final liver segmentation is produced by a shape–intensity prior level set in the most likely liver region. Our method is evaluated and demonstrated on 25 test CT datasets from our partner site, and its results are compared with two state-of-the-art liver segmentation methods. Moreover, our performance results on 10 MICCAI test datasets are submitted to the organizers for comparison with the other automatic algorithms.

Results

Using the 25 test CT datasets, average symmetric surface distance is \(1.09 \pm 0.34\) mm (range 0.62–2.12 mm), root mean square symmetric surface distance error is \(1.72 \pm 0.46\) mm (range 0.97–3.01 mm), and maximum symmetric surface distance error is \(18.04 \pm 3.51\) mm (range 12.73–26.67 mm) by our method. Our method on 10 MICCAI test data sets ranks 10th in all the 47 automatic algorithms on the site as of July 2015. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our method is a promising tool to improve the efficiency of both techniques.

Conclusion

The applicability of the proposed method to some challenging clinical problems and the segmentation of the liver are demonstrated with good results on both quantitative and qualitative experimentations. This study suggests that the proposed framework can be good enough to replace the time-consuming and tedious slice-by-slice manual segmentation approach.

     

A hybrid approach for segmentation of anatomic structures in medical images

Objective

We propose a hybrid interactive approach for the segmentation of anatomic structures in medical images with higher accuracy at lower user interaction cost.

Materials and methods

Eighteen brain MR scans from the Internet Brain Segmentation Repository are used for brain structure segmentation. A MR scan and a CT scan of an old female are used for orbital structure segmentation. The proposed approach combines shape-based interpolation, radial basis function (RBF)-based warping and model-based segmentation. With this approach, to segment a structure in a 3D image, we first delineate the structure in several slices using interactive methods, and then use shape-based interpolation to automatically generate an initial 3D model of the structure from the segmented slices. To refine the initial model, we specify a set of additional points on the structure boundary in the image, and use a RBF to warp the model so that it passes the specified points. Finally, we adopt a point-anchored active surface approach to further deform the model for a better fitting of the model with its corresponding structure in image.

Results

Two brain structures and 15 orbital structures are segmented. For each structure, it needs only to semi- automatically segment three to five 2D slices and specify two to nine additional points on the structure boundary. The time cost for each structure is about 1–3 min. The overlap ratio of the segmentation results and the ground truth is higher than 96%.

Conclusion

The proposed method for the segmentation of anatomic structure achieved higher accuracy at lower user interaction cost, and therefore promising in many applications such as surgery planning and simulation, atlas construction, and morphometric analysis of anatomic structures.     

Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification

Purpose

Automatic segmentation of the retinal vasculature is a first step in computer-assisted diagnosis and treatment planning. The extraction of retinal vessels in pediatric retinal images is challenging because of comparatively wide arterioles with a light streak running longitudinally along the vessel’s center, the central vessel reflex. A new method for automatic segmentation was developed and tested.

Method

   A supervised method for retinal vessel segmentation in the images of multi-ethnic school children was developed based on ensemble classifier of bootstrapped decision trees. A collection of dual Gaussian, second derivative of Gaussian and Gabor filters, along with the generalized multiscale line strength measure and morphological transformation is used to generate the feature vector. The feature vector encodes information to handle the normal vessels as well as the vessels with the central reflex. The methodology is evaluated on CHASE_DB1, a relatively new public retinal image database of multi-ethnic school children, which is a subset of retinal images from the Child Heart and Health Study in England (CHASE) dataset.

Results

   The segmented retinal images from the CHASE_DB1 database produced best case accuracy, sensitivity and specificity of 0.96, 0.74 and 0.98, respectively, and worst case measures of 0.94, 0.67 and 0.98, respectively.

Conclusion

   A new retinal blood vessel segmentation algorithm was developed and tested with a shared database. The observed accuracy, speed, robustness and simplicity suggest that the algorithm may be a suitable tool for automated retinal image analysis in large population-based studies.     

Catheter tip tracking for MR-guided interventions using discrete Kalman filter and mean shift localization

Purpose

This paper presents and evaluates stochastic computer algorithms used to automatically detect and track marked catheter tip during MR-guided catheterization. The algorithms developed employ extraction and matching of regional features of the catheter tip to perform the localization.

Method

To perform the tracking, a probability map that indicates the possible locations of the catheter tip in the MR images is first generated. This map is generated from the similarity to a given marker template. The method to assess the similarity between the marker template image and the different positions on each MR frame is based on speeded-up robust features extracted from the gradient image. The probability map is then used in two different stochastic localization frameworks mean shift (MS) localization and Kalman filter (KF) to track the position of the catheter using pairs of orthogonal projection of 2D MR images. The algorithm developed was tested on catheter tip marked with LC resonant circuit (of size $2\,\hbox {mm}\,\times \,2\,\hbox {cm}$ ) tuned to the Larmor frequency of the MRI scanner and for different image resolutions (1, 3, 5 and 7 mm squared pixel).

Results

The tracking performance was very robust for the two algorithms MS and KF with image resolution as low as 3 mm where the localization error was about 1 mm for KF and 0.9 mm for MS. For the 5-mm resolution images, the error was 2.2 mm for both KF and MS, and for the 7-mm resolution images, the error was 3.6 and 3.7 mm for KF and MS, respectively.

Conclusion

Both KF and MS gave comparable results when it comes to accuracy for the different image resolutions. The results showed that the two tracking algorithms tracked the catheter tip with high robustness for image resolution of 3 mm and with acceptable reliability for image resolution as poor as 5 mm with the resonant marker configuration used.     

Combined surface and volume processing for fused joint segmentation

Purpose

Segmentation of rheumatoid joints from CT images is a complicated task. The pathological state of the joint results in a non-uniform density of the bone tissue, with holes and irregularities complicating the segmentation process. For the specific case of the shoulder joint, existing segmentation techniques often fail and lead to poor results. This paper describes a novel method for the segmentation of these joints.

Methods

Given a rough surface model of the shoulder, a loop that encircles the joint is extracted by calculating the minimum curvature of the surface model. The intersection points of this loop with the separate CT-slices are connected by means of a path search algorithm. Inaccurate sections are corrected by iteratively applying a Hough transform to the segmentation result.

Results

As a qualitative measure we calculated the Dice coefficient and Hausdorff distances of the automatic segmentations and expert manual segmentations of CT-scans of ten severely deteriorated shoulder joints. For the humerus and scapula the median Dice coefficient was 98.9% with an interquartile range (IQR) of 95.8–99.4 and 98.5% (IQR 98.3–99.2%), respectively. The median Hausdorff distances were 3.06 mm (IQR 2.30–4.14) and 3.92 mm (IQR 1.96 –5.92 mm), respectively.

Conclusion

The routine satisfies the criterion of our particular application to accurately segment the shoulder joint in under 2 min. We conclude that combining surface curvature, limited user interaction and iterative refinement via a Hough transform forms a satisfactory approach for the segmentation of severely damaged arthritic shoulder joints.     

Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter

Purpose

Existing computer-aided detection schemes for lung nodule detection require a large number of calculations and tens of minutes per case; there is a large gap between image acquisition time and nodule detection time. In this study, we propose a fast detection scheme of lung nodule in chest CT images using cylindrical nodule-enhancement filter with the aim of improving the workflow for diagnosis in CT examinations.

Methods

Proposed detection scheme involves segmentation of the lung region, preprocessing, nodule enhancement, further segmentation, and false-positive (FP) reduction. As a nodule enhancement, our method employs a cylindrical shape filter to reduce the number of calculations. False positives (FPs) in nodule candidates are reduced using support vector machine and seven types of characteristic parameters.

Results

The detection performance and speed were evaluated experimentally using Lung Image Database Consortium publicly available image database. A 5-fold cross-validation result demonstrates that our method correctly detects 80 % of nodules with 4.2 FPs per case, and detection speed of proposed method is also 4–36 times faster than existing methods.

Conclusion

Detection performance and speed indicate that our method may be useful for fast detection of lung nodules in CT images.     

Automated implant segmentation in cone-beam CT using edge detection and particle counting

Purpose

To develop a fully automated, accurate and robust segmentation technique for dental implants on cone-beam CT (CBCT) images.

Methods

A head-size cylindrical polymethyl methacrylate phantom was used, containing titanium rods of 5.15 mm diameter. The phantom was scanned on 17 CBCT devices, using a total of 39 exposure protocols. Images were manually thresholded to verify the applicability of adaptive thresholding and to determine a minimum threshold value \(({T}_{\mathrm{min}})\) . A three-step automatic segmentation technique was developed. Firstly, images were pre-thresholded using \({T}_{\mathrm{min}}\) . Next, edge enhancement was performed by filtering the image with a Sobel operator. The filtered image was thresholded using an iteratively determined fixed threshold \(({T}_{\mathrm{edge}})\) and converted to binary. Finally, a particle counting method was used to delineate the rods. The segmented area of the titanium rods was compared to the actual area, which was corrected for phantom tilting.

Results

Manual thresholding resulted in large variation in threshold values between CBCTs. After applying the edge-enhancing filter, a stable \({T}_{\mathrm{edge}}\) value of 7.5 % was found. Particle counting successfully detected the rods for all but one device. Deviations between the segmented and real area ranged between \(-\) 2.7 and + \(14.4\,\hbox {mm}^{2}\) with an average absolute error of \(2.8\,\hbox {mm}^{2}\) . Considering the diameter of the segmented area, submillimeter accuracy was seen for all but two data sets.

Conclusion

A segmentation technique was defined which can be applied to CBCT data for an accurate and fully automatic delineation of titanium rods. The technique was validated in vitro and will be further tested and refined on patient data.     

Coronal reformatted CT images contribute to the precise evaluation of the radiofrequency ablative margin for hepatocellular carcinoma

Objective

The purpose of the present study was to evaluate the usefulness of coronal reformatted images obtained from 64-slice multi-detector computed tomography to assess the ablative margin (AM) in hepatocellular carcinoma (HCC) treated with radio frequency ablation (RFA).

Methods

Ninety-five HCC nodules were analyzed in 66 HCC patients treated with RFA. Two radiologists and one hepatologist independently reviewed axial CT images with or without coronal reformatted images in HCC treated with RFA. Nodules were determined as AM-sufficient (≥5 mm) or AM-insufficient (<5 mm). The level of interobserver agreement was measured using the weighted kappa test. The sensitivity, specificity, and positive and negative predictive values (NPVs) of an insufficient AM (<5 mm) to predict local recurrence were evaluated.

Results

The numbers of AM-sufficient nodules judged by readers 1–3 based on axial images and both axial and coronal images were 56, 49, and 58, and 47, 33, and 48, respectively. Excellent agreement and good to excellent agreement were obtained among the three readers on axial image readings and both axial and coronal image readings, respectively. The mean sensitivity, specificity, and positive and NPVs of an insufficient AM on axial images and both axial and coronal images to predict local recurrence were 64%, 60%, 17%, and 93%, and 95%, 50%, 20%, and 97%, respectively.

Conclusions

Coronal reformatted CT images should be utilized to evaluate the AM in HCC treated with RFA in order to decrease the risk of local recurrence following treatment.     

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs

Purpose

   Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs.

Methods

   Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence.

Results

   A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was $0.96\,\hbox {mm} \pm 0.35\,\hbox {mm}$ , requiring $<\!5$  s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference.

Conclusions

   A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.