User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy.

Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to provide satisfactory result, and post-processing corrections are often needed. Semi-automatic segmentation methods are designed to overcome these problems by combining physicians' expertise and computers' potential. This study evaluates two semi-automatic segmentation methods with different types of user interactions, named the "strokes" and the "contour", to provide insights into the role and impact of human-computer interaction. Two physicians participated in the experiment. In total, 42 case studies were carried out on five different types of organs at risk. For each case study, both the human-computer interaction process and quality of the segmentation results were measured subjectively and objectively. Furthermore, different measures of the process and the results were correlated. A total of 36 quantifiable and ten non-quantifiable correlations were identified for each type of interaction. Among those pairs of measures, 20 of the contour method and 22 of the strokes method were strongly or moderately correlated, either directly or inversely. Based on those correlated measures, it is concluded that: (1) in the design of semi-automatic segmentation methods, user interactions need to be less cognitively challenging; (2) based on the observed workflows and preferences of physicians, there is a need for flexibility in the interface design; (3) the correlated measures provide insights that can be used in improving user interaction design.

Segmentation of multiple heart cavities in 3-D transesophageal ultrasound images.

Three-dimensional transesophageal echocardiography (TEE) is an excellent modality for real-time visualization of the heart and monitoring of interventions. To improve the usability of 3-D TEE for intervention monitoring and catheter guidance, automated segmentation is desired. However, 3-D TEE segmentation is still a challenging task due to the complex anatomy with multiple cavities, the limited TEE field of view, and typical ultrasound artifacts. We propose to segment all cavities within the TEE view with a multi-cavity active shape model (ASM) in conjunction with a tissue/blood classification based on a gamma mixture model (GMM). 3-D TEE image data of twenty patients were acquired with a Philips X7-2t matrix TEE probe. Tissue probability maps were estimated by a two-class (blood/tissue) GMM. A statistical shape model containing the left ventricle, right ventricle, left atrium, right atrium, and aorta was derived from computed tomography angiography (CTA) segmentations by principal component analysis. ASMs of the whole heart and individual cavities were generated and consecutively fitted to tissue probability maps. First, an average whole-heart model was aligned with the 3-D TEE based on three manually indicated anatomical landmarks. Second, pose and shape of the whole-heart ASM were fitted by a weighted update scheme excluding parts outside of the image sector. Third, pose and shape of ASM for individual heart cavities were initialized by the previous whole heart ASM and updated in a regularized manner to fit the tissue probability maps. The ASM segmentations were validated against manual outlines by two observers and CTA derived segmentations. Dice coefficients and point-to-surface distances were used to determine segmentation accuracy. ASM segmentations were successful in 19 of 20 cases. The median Dice coefficient for all successful segmentations versus the average observer ranged from 90% to 71% compared with an inter-observer range of 95% to 84%. The agreement against the CTA segmentations was slightly lower with a median Dice coefficient between 85% and 57%. In this work, we successfully showed the accuracy and robustness of the proposed multi-cavity segmentation scheme. This is a promising development for intraoperative procedure guidance, e.g., in cardiac electrophysiology.

Additional diagnostic value of integrated analysis of cardiac CTA and SPECT MPI using the SMARTVis system in patients with suspected coronary artery disease.

UNLABELLED: CT angiography (CTA) and SPECT myocardial perfusion imaging (MPI) are complementary imaging techniques to assess coronary artery disease (CAD). Spatial integration and combined visualization of SPECT MPI and CTA data may facilitate correlation of myocardial perfusion defects and subtending coronary arteries and thus offer additional diagnostic value over either stand-alone or side-by-side interpretation of the respective datasets from the 2 modalities. In this study, we investigated the additional diagnostic value of a software-based CTA/SPECT MPI image fusion system over conventional side-by-side analysis in patients with suspected CAD. METHODS: Seventeen symptomatic patients who underwent both CTA and SPECT MPI within a 90-d period were included in our study; 7 of them also underwent invasive coronary angiography (ICA). The potential benefits of the synchronized multimodal heart visualization (SMARTVis) system in assessing CAD were investigated through a case study involving 4 experts from 2 medical centers, in which we performed, first, a side-by-side analysis using structured CTA and SPECT reports and, second, an integrated analysis using the SMARTVis system in addition to the reports. RESULTS: The fused interpretation led to a more accurate diagnosis, reflected in an increase in the individual observers' sensitivity and specificity to correctly refer for invasive angiography eventually followed by revascularization. For the first, second, third, and fourth observers, the respective sensitivities improved from 50%, 60%, 80%, and 80% to 70%, 80%, 100%, and 90% and the respective specificities from 100%, 94%, 83%, and 83% to 100%, 100%, 94%, and 83%. Additionally, the interobserver diagnosis agreement increased from 74% to 84%. The improvement was primarily found in patients presenting with CAD in more vessels than the number of reported perfusion defects. CONCLUSION: Integrated analysis of cardiac CTA and SPECT MPI using the SMARTVis system results in an improved diagnostic performance.

Automatic quantification of epicardial fat volume on non-enhanced cardiac CT scans using a multi-atlas segmentation approach.

PURPOSE: There is increasing evidence that epicardial fat (i.e., adipose tissue contained within the pericardium) plays an important role in the development of cardiovascular disease. Obtaining the epicardial fat volume from routinely performed non-enhanced cardiac CT scans is therefore of clinical interest. The purpose of this work is to investigate the feasibility of automatic pericardium segmentation and subsequent quantification of epicardial fat on non-enhanced cardiac CT scans. METHODS: Imaging data of 98 randomly selected subjects belonging to a larger cohort of subjects who underwent a cardiac CT scan at our medical center were retrieved. The data were acquired on two different scanners. Automatic multi-atlas based method for segmenting the pericardium and calculating the epicardial fat volume has been developed. The performance of the method was assessed by (1) comparing the automatically segmented pericardium to a manually annotated reference standard, (2) comparing the automatically obtained epicardial fat volumes to those obtained manually, and (3) comparing the accuracy of the automatic results to the inter-observer variability. RESULTS: Automatic segmentation of the pericardium was achieved with a Dice similarity index of 89.1 ± 2.6% with respect to Observer 1 and 89.2 ± 1.9% with respect to Observer 2. The correlation between the automatic method and the manual observers with respect to the epicardial fat volume computed as the Pearson's correlation coefficient (R) was 0.91 (P < 0.001) for both observers. The inter-observer study resulted in a Dice similarity index of 89.0 ± 2.4% for segmenting the pericardium and a Pearson's correlation coefficient of 0.92 (P<0.001) for computation of the epicardial fat volume. CONCLUSIONS: The authors developed a fully automatic method that is capable of segmenting the pericardium and quantifying epicardial fat on non-enhanced cardiac CT scans. The authors demonstrated the feasibility of using this method to replace manual annotations by showing that the automatic method performs as good as manual annotation on a large dataset.

Automatic segmentation, detection and quantification of coronary artery stenoses on CTA.

Accurate detection and quantification of coronary artery stenoses is an essential requirement for treatment planning of patients with suspected coronary artery disease. We present a method to automatically detect and quantify coronary artery stenoses in computed tomography coronary angiography. First, centerlines are extracted using a two-point minimum cost path approach and a subsequent refinement step. The resulting centerlines are used as an initialization for lumen segmentation, performed using graph cuts. Then, the expected diameter of the healthy lumen is estimated by applying robust kernel regression to the coronary artery lumen diameter profile. Finally, stenoses are detected and quantified by computing the difference between estimated and expected diameter profiles. We evaluated our method using the data provided in the Coronary Artery Stenoses Detection and Quantification Evaluation Framework. Using 30 testing datasets, the method achieved a detection sensitivity of 29% and a positive predictive value (PPV) of 24% as compared to quantitative coronary angiography (QCA), and a sensitivity of 21% and a PPV of 23% as compared manual assessment based on consensus reading of CTA by 3 observers. The stenoses degree was estimated with an absolute average difference of 31%, a root mean square difference of 39.3% when compared to QCA, and a weighted kappa value of 0.29 when compared to CTA. A Dice of 68 and 65% was reported for lumen segmentation of healthy and diseased vessel segments respectively. According to the ranking of the evaluation framework, our method finished fourth for stenosis detection, second for stenosis quantification and second for lumen segmentation.

Regression-based cardiac motion prediction from single-phase CTA.

State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of 2.3±0.5 mm, compared to values of 2.7±0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure.

Cardiac MR perfusion image processing techniques: a survey.

First-pass cardiac MR perfusion (CMRP) imaging has undergone rapid technical advancements in recent years. Although the efficacy of CMRP imaging in the assessment of coronary artery diseases (CAD) has been proven, its clinical use is still limited. This limitation stems, in part, from manual interaction required to quantitatively analyze the large amount of data. This process is tedious, time-consuming, and prone to operator bias. Furthermore, acquisition and patient related image artifacts reduce the accuracy of quantitative perfusion assessment. With the advent of semi- and fully automatic image processing methods, not only the challenges posed by these artifacts have been overcome to a large extent, but a significant reduction has also been achieved in analysis time and operator bias. Despite an extensive literature on such image processing methods, to date, no survey has been performed to discuss this dynamic field. The purpose of this article is to provide an overview of the current state of the field with a categorical study, along with a future perspective on the clinical acceptance of image processing methods in the diagnosis of CAD.

Robust motion correction in the frequency domain of cardiac MR stress perfusion sequences.

First-pass cardiac MR perfusion (CMRP) imaging allows identification of hypo-perfused areas in the myocardium and therefore helps in early detection of coronary artery disease (CAD). However, its efficacy is often limited by respiratory motion artifacts, especially in stress-induced sequences. These distortions lead to unreliable estimates of perfusion linked parameters, such as the myocardial perfusion reserve index (MPRI). We propose a novel, robust motion correction method that suppresses motion artifacts in the frequency domain. The method is validated using rest and stress perfusion datasets of 10 patients and is compared to a state-of-the-art independent component analysis based method. Contrary to the latter, the proposed method reduces the remaining motion to less than the pixel size and allows the reliable computation of the MPRI. The strong agreement between perfusion parameters based on expert contours and after applying the proposed method enables the near-automated quantitative analyses of stress MR perfusion sequences in a clinical setting.

Comprehensive visualization of multimodal cardiac imaging data for assessment of coronary artery disease: first clinical results of the SMARTVis tool.

PURPOSE: In clinical practice, both coronary anatomy and myocardial perfusion information are needed to assess coronary artery disease (CAD). The extent and severity of coronary stenoses can be determined using computed tomography coronary angiography (CTCA); the presence and amount of ischemia can be identified using myocardial perfusion imaging, such as perfusion magnetic resonance imaging (PMR). To determine which specific stenosis is associated with which ischemic region, experts use assumptions on coronary perfusion territories. Due to the high variability between patient's coronary artery anatomies, as well as the uncertain relation between perfusion territories and supplying coronary arteries, patient-specific systems are needed. MATERIAL AND METHODS: We present a patient-specific visualization system, called Synchronized Multimodal heART Visualization (SMARTVis), for relating coronary stenoses and perfusion deficits derived from CTCA and PMR, respectively. The system consists of the following comprehensive components: (1) two or three-dimensional fusion of anatomical and functional information, (2) automatic detection and ranking of coronary stenoses, (3) estimation of patient-specific coronary perfusion territories. RESULTS: The potential benefits of the SMARTVis tool in assessing CAD were investigated through a case-study evaluation (conventional vs. SMARTVis tool): two experts analyzed four cases of patients with suspected multivessel coronary artery disease. When using the SMARTVis tool, a more reliable estimation of the relation between perfusion deficits and stenoses led to a more accurate diagnosis, as well as a better interobserver diagnosis agreement. CONCLUSION: The SMARTVis comprehensive visualization system can be effectively used to assess disease status in multivessel CAD patients, offering valuable new options for the diagnosis and management of these patients.

Conditional shape models for cardiac motion estimation.

We propose a conditional statistical shape model to predict patient specific cardiac motion from the 3D end-diastolic CTA scan. The model is built from 4D CTA sequences by combining atlas based segmentation and 4D registration. Cardiac motion estimation is, for example, relevant in the dynamic alignment of pre-operative CTA data with intra-operative X-ray imaging. Due to a trend towards prospective electrocardiogram gating techniques, 4D imaging data, from which motion information could be extracted, is not commonly available. The prediction of motion from shape information is thus relevant for this purpose. Evaluation of the accuracy of the predicted motion was performed using CTA scans of 50 patients, showing an average accuracy of 1.1 mm.