Cardiac Substructure Segmentation and Dosimetry Using a Novel Hybrid Magnetic Resonance and Computed Tomography Cardiac Atlas

Cardiac Disease in Childhood Cancer Survivors Treated With Radiation Therapy: A PENTEC Comprehensive Review

PURPOSE

Radiation therapy (RT) is an essential component in the treatment of many pediatric malignancies. Thoracic RT may expose the heart to radiation dose and thereby increase the risk of late cardiac disease. This comprehensive review from the Pediatric Normal Tissue Effects in the Clinic (PENTEC) initiative focused on late cardiac disease in survivors of childhood cancer treated with RT.

METHODS AND MATERIALS

This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We identified 1496 articles; 4 were included for dose-response modeling between mean cardiac radiation dose and risk of late coronary artery disease, heart failure (HF), valvular disease, and any cardiac disease.

RESULTS

For each 10-Gy increase in corrected mean cardiac radiation dose in 1.8- to 2.0-Gy fractions, we estimated a hazard ratio of 2.01 (95% confidence interval [CI], 1.79-2.25) for coronary artery disease, of 1.87 (95% CI, 1.70-2.06) for HF, of 1.87 (95% CI, 1.78-1.96) for valvular disease, and of 1.88 (95% CI, 1.75-2.03) for any cardiac disease. From the same model, for each 100-mg/m2 increase in cumulative anthracycline dose, the hazard ratio for the development of HF was 1.93 (95% CI, 1.58-2.36), equivalent to an increase in mean heart dose of approximately 10.5 Gy. Other nontreatment factors were inconsistently reported in the analyzed articles.

CONCLUSIONS

Radiation dose to the heart increases the risk of late cardiac disease, but survivors of childhood cancer who receive a mean dose <10 Gy at standard fractionation are at low absolute risk (<∼2% approximately 30 years after exposure) of late cardiac disease in the absence of anthracycline exposure. Minimizing cardiac radiation dose is especially relevant in children receiving anthracyclines. When cardiac sparing is not possible, we recommend prioritizing target coverage. It is likely that individual cardiac substructure doses will be a better predictor of specific cardiac diseases than mean dose, and we urge the pediatric oncology community to further study these relationships.

Cardiac substructure delineation in radiation therapy - A state-of-the-art review

Delineation of cardiac substructures is crucial for a better understanding of radiation-related cardiotoxicities and to facilitate accurate and precise cardiac dose calculation for developing and applying risk models. This review examines recent advancements in cardiac substructure delineation in the radiation therapy (RT) context, aiming to provide a comprehensive overview of the current level of knowledge, challenges and future directions in this evolving field. Imaging used for RT planning presents challenges in reliably visualising cardiac anatomy. Although cardiac atlases and contouring guidelines aid in standardisation and reduction of variability, significant uncertainties remain in defining cardiac anatomy. Coupled with the inherent complexity of the heart, this necessitates auto-contouring for consistent large-scale data analysis and improved efficiency in prospective applications. Auto-contouring models, developed primarily for breast and lung cancer RT, have demonstrated performance comparable to manual contouring, marking a significant milestone in the evolution of cardiac delineation practices. Nevertheless, several key concerns require further investigation. There is an unmet need for expanding cardiac auto-contouring models to encompass a broader range of cancer sites. A shift in focus is needed from ensuring accuracy to enhancing the robustness and accessibility of auto-contouring models. Addressing these challenges is paramount for the integration of cardiac substructure delineation and associated risk models into routine clinical practice, thereby improving the safety of RT for future cancer patients.

Cardiac segments dosimetric benefit from deep inspiration breath hold technique for left-sided breast cancer radiotherapy

Background

The objective was to compare dosimetry in left-sided breast cancer (LSBC) patients receiving deep inspiration breath hold (DIBH) radiotherapy (RT) with free-breathing (FB) treatment plans.

Materials and methods

Voluntary DIBH with a spirometer-based video-assisted system and CT-simulation were performed under FB and DIBH conditions on 40 LSBC patients, segmented according Duane's atlas. IMRT plans kept the same dosimetric goals on FB and DIBH conditions. Target, lungs and heart volumes were measured. Planning target volume (PTV) dose distribution, organs at risk (OARs) dose/volume parameters, including cardiac substructures, were calculated.

Results

Lungs and left-lung volumes increased in DIBH conditions (ΔV = 1637.8 ml ± 555.3 and 783.5 ml ± 286.4, respectively). Heart volume slightly decreased in apnea (p = 0.04), but target volumes, CTV and PTV were similar in FB or DIBH plans. PTV dose coverage was similar irrespective of respiratory conditions (median D50% = 41.1 Gy vs 41.0 Gy, p = 0.665; V95% = 96.9% vs. 97%). Mean dose for the whole heart (MHD), left ventricle (LV), and LV segments were significantly reduced in DIBH plans. V20 values for heart subvolumes were significantly different only for those that received considerable doses (apical and anterior). DIBH plans provided significantly smaller doses (Dmax, D2%, and V20) to the LAD artery.

Conclusion

Important dosimetric improvements can be achieved with DIBH technique for LSBC patients, reducing the dose to the LAD artery and heart, particularly to the segments closer to the chest wall. Apical/anterior LV segments, should be considered as separate organ at risk in breast RT.

How to contour the different heart subregions for future deep-learning modeling of the heart: A practical pictorial proposal for radiation oncologists

There are currently no accurate rules for manually delineating the subregions of the heart (cavities, vessels, aortic/mitral valves, Planning organ at Risk Volumes for coronary arteries) with the perspective of deep-learning based modeling. Our objective was to present a practical pictorial view for radiation oncologists, based on the RTOG atlas and anatomical complementary considerations for the cases where the RTOG guidelines are missing.

An open source auto-segmentation algorithm for delineating heart and substructures - Development and validation within a multicenter lung cancer cohort

BACKGROUND AND PURPOSE

Irradiation of the heart in thoracic cancers raises toxicity concerns. For accurate dose estimation, automated heart and substructure segmentation is potentially useful. In this study, a hybrid automatic segmentation is developed. The accuracy of delineation and dose predictions were evaluated, testing the method's potential within heart toxicity studies.

MATERIALS AND METHODS

The hybrid segmentation method delineated the heart, four chambers, three large vessels, and the coronary arteries. The method consisted of a nnU-net heart segmentation and partly atlas- and model-based segmentation of the substructures. The nnU-net training and atlas segmentation was based on lung cancer patients and was validated against a national consensus dataset of 12 patients with breast cancer. The accuracy of dose predictions between manual and auto-segmented heart and substructures was evaluated by transferring the dose distribution of 240 previously treated lung cancer patients to the consensus data set.

RESULTS

The hybrid auto-segmentation method performed well with a heart dice similarity coefficient (DSC) of 0.95, with no statistically significant difference between the automatic and manual delineations. The DSC for the chambers varied from 0.78-0.86 for the automatic segmentation and was comparable with the inter-observer variability. Most importantly, the automatic segmentation was as precise as the clinical experts in predicting the dose distribution to the heart and all substructures.

CONCLUSION

The hybrid segmentation method performed well in delineating the heart and substructures. The prediction of dose by the automatic segmentation was aligned with the manual delineations, enabling measurement of heart and substructure dose in large cohorts. The delineation algorithm will be available for download.

Development of a comprehensive cardiac atlas on a 1.5 Tesla Magnetic Resonance Linear Accelerator

Background and purpose

The 1.5 Tesla (T) Magnetic Resonance Linear Accelerator (MRL) provides an innovative modality for improved cardiac imaging when planning radiation treatment. No MRL based cardiac atlases currently exist, thus, we sought to comprehensively characterize cardiac substructures, including the conduction system, from cardiac images acquired using a 1.5 T MRL and provide contouring guidelines.

Materials and methods

Five volunteers were enrolled in a prospective protocol (NCT03500081) and were imaged on the 1.5 T MRL with Half Fourier Single-Shot Turbo Spin-Echo (HASTE) and 3D Balanced Steady-State Free Precession (bSSFP) sequences in axial, short axis, and vertical long axis. Cardiac anatomy was contoured by (AS) and confirmed by a board certified cardiologist (JR) with expertise in cardiac MR imaging.

Results

A total of five volunteers had images acquired with the HASTE sequence, with 21 contours created on each image. One of these volunteers had additional images obtained with 3D bSSFP sequences in the axial plane and additional images obtained with HASTE sequences in the key cardiac planes. Contouring guidelines were created and outlined. 15-16 contours were made for the short axis and vertical long axis. The cardiac conduction system was demonstrated with eleven representative contours. There was reasonable variation of contour volume across volunteers, with structures more clearly delineated on the 3D bSSFP sequence.

Conclusions

We present a comprehensive cardiac atlas using novel images acquired prospectively on a 1.5 T MRL. This cardiac atlas provides a novel resource for radiation oncologists in delineating cardiac structures for treatment with radiotherapy, with special focus on the cardiac conduction system.

ASSET: Auto-Segmentation of the Seventeen SEgments for Ventricular Tachycardia Ablation in Radiation Therapy

There has been a recent effort to treat high-risk ventricular tachycardia (VT) patients through radio-ablation. However, manual segmentation of the VT target is complex and time-consuming. This work introduces ASSET, or Auto-segmentation of the Seventeen SEgments for Tachycardia ablation, to aid in radiation therapy (RT) planning. ASSET was retrospectively applied to CTs for 26 thoracic RT patients (13 undergoing VT ablation). The physician-defined parasternal long-axis of the left ventricle (LV) and the axes generated from principal component analysis (PCA) were compared using mean distance to agreement (MDA) and angle of separation. The manually selected right ventricle insertion point and LVs were used to apply the ASSET model to automatically generate the 17 segments of the LV myocardium (LVM). Physician-defined parasternal long-axis differed from PCA by 1.2 ± 0.3 mm MDA and 6.9 ± 0.7 degrees. Segments differed by 0.69 ± 0.29 mm MDA and 0.89 ± 0.03 Dice similarity coefficient. Running ASSET takes <5 min where manual segmentation took >2 h/patient. Agreement between ASSET and expert contours was comparable to inter-observer variability. Qualitative scoring conducted by three experts revealed automatically generated segmentations were clinically useable as-is. ASSET offers efficient and reliable automatic segmentations for the 17 segments of the LVM for target generation in RT planning.

Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy

Radiotherapy (RT) doses to cardiac substructures from the definitive treatment of locally advanced non-small cell lung cancers (NSCLC) have been linked to post-RT cardiac toxicities. With modern treatment delivery techniques, it is possible to focus radiation doses to the planning target volume while reducing cardiac substructure doses. However, it is often challenging to design such treatment plans due to complex tradeoffs involving numerous cardiac substructures. Here, we built a cardiac-substructure-based knowledge-based planning (CS-KBP) model and retrospectively evaluated its performance against a cardiac-based KBP (C-KBP) model and manually optimized patient treatment plans. CS-KBP/C-KBP models were built with 27 previously-treated plans that preferentially spare the heart. While the C-KBP training plans were created with whole heart structures, the CS-KBP model training plans each have 15 cardiac substructures (coronary arteries, valves, great vessels, and chambers of the heart). CS-KBP training plans reflect cardiac-substructure sparing preferences. We evaluated both models on 28 additional patients. Three sets of treatment plans were compared: (1) manually optimized, (2) C-KBP model-generated, and (3) CS-KBP model-generated. Plans were normalized to receive the prescribed dose to at least 95% of the PTV. A two-tailed paired-sample t-test was performed for clinically relevant dose-volume metrics to evaluate the performance of the CS-KBP model against the C-KBP model and clinical plans, respectively. Overall results show significantly improved cardiac substructure sparing by CS-KBP in comparison to C-KBP and the clinical plans. For instance, the average left anterior descending artery volume receiving 15 Gy (V15 Gy) was significantly lower (p &lt; 0.01) for CS-KBP (0.69 ± 1.57 cc) compared to the clinical plans (1.23 ± 1.76 cc) and C-KBP plans (1.05 ± 1.68 cc). In conclusion, the CS-KBP model significantly improved cardiac-substructure sparing without exceeding the tolerances of other OARs or compromising PTV coverage.

Validation of an established deep learning auto-segmentation tool for cardiac substructures in 4D radiotherapy planning scans

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Cardiotoxicity is a common complication of lung cancer radiotherapy.

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Segmentation of cardiac substructures is time-consuming and challenging.

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Deep learning segmentation tools can perform this task in 3D and 4D scans.

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Performance is high when assessed geometrically, dosimetrically and clinically.

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Auto-segmentation tools may accelerate clinical workflows and enable research.

Background

Emerging data suggest that dose-sparing several key cardiac regions is prognostically beneficial in lung cancer radiotherapy. The cardiac substructures are challenging to contour due to their complex geometry, poor soft tissue definition on computed tomography (CT) and cardiorespiratory motion artefact. A neural network was previously trained to generate the cardiac substructures using three-dimensional radiotherapy planning CT scans (3D-CT). In this study, the performance of that tool on the average intensity projection from four-dimensional (4D) CT scans (4D-AVE), now commonly used in lung radiotherapy, was evaluated.

Materials and Methods

The 4D-AVE of n=20 patients completing radiotherapy for lung cancer 2015–2020 underwent manual and automated cardiac substructure segmentation. Manual and automated substructures were compared geometrically and dosimetrically. Two senior clinicians also qualitatively assessed the auto-segmentation tool’s output.

Results

Geometric comparison of the automated and manual segmentations exhibited high levels of similarity across parameters, including volume difference (11.8% overall) and Dice similarity coefficient (0.85 overall), and were consistent with 3D-CT performance. Differences in mean (median 0.2 Gy, range −1.6–0.3 Gy) and maximum (median 0.4 Gy, range −2.2–0.9 Gy) doses to substructures were generally small. Nearly all structures (99.5 %) were deemed to be appropriate for clinical use without further editing.

Conclusions

Cardiac substructure auto-segmentation using a deep learning-based tool trained on a 3D-CT dataset was feasible on the 4D-AVE scan, meaning this tool is suitable for use on 4D-CT radiotherapy planning scans. Application of this tool would increase the practicality of routine clinical cardiac substructure delineation, and enable further cardiac radiation effects research.

Characterizing Sensitive Cardiac Substructure Excursion Due to Respiration

Purpose

Whole-heart dose metrics are not as strongly linked to late cardiac morbidities as radiation doses to individual cardiac substructures. Our aim was to characterize the excursion and dosimetric variation throughout respiration of sensitive cardiac substructures for future robust safety margin design.

Methods and Materials

Eleven patients with cancer treatments in the thorax underwent 4-phase noncontrast 4-dimensional computed tomography (4DCT) with T2-weighted magnetic resonance imaging in end-exhale. The end-exhale phase of the 4DCT was rigidly registered with the magnetic resonance imaging and refined with an assisted alignment surrounding the heart from which 13 substructures (chambers, great vessels, coronary arteries, etc) were contoured by a radiation oncologist on the 4DCT. Contours were deformed to the other respiratory phases via an intensity-based deformable registration for radiation oncologist verification. Measurements of centroid and volume were evaluated between phases. Mean and maximum dose to substructures were evaluated across respiratory phases for the breast (n = 8) and thoracic cancer (n = 3) cohorts.

Results

Paired t tests revealed reasonable maintenance of geometric and anatomic properties (P < .05 for 4/39 volume comparisons). Maximum displacements >5 mm were found for 24.8%, 8.5%, and 64.5% of the cases in the left-right, anterior-posterior, and superior-inferior axes, respectively. Vector displacements were largest for the inferior vena cava and the right coronary artery, with displacements up to 17.9 mm. In breast, the left anterior descending artery D_mean varied 3.03 ± 1.75 Gy (range, 0.53-5.18 Gy) throughout respiration whereas lung showed patient-specific results. Across all patients, whole heart metrics were insensitive to breathing phase (mean and maximum dose variations <0.5 Gy).

Conclusions

This study characterized the intrafraction displacement of the cardiac substructures through the respiratory cycle and highlighted their increased dosimetric sensitivity to local dose changes not captured by whole heart metrics. Results suggest value of cardiac substructure margin generation to enable more robust cardiac sparing and to reduce the effect of respiration on overall treatment plan quality.

Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study

BACKGROUND

Adjuvant radiation therapy improves the overall survival and loco-regional control in patients with breast cancer. However, radiation-induced heart disease, which occurs after treatment from incidental radiation exposure to the cardiac organ, is an emerging challenge. This study aimed to generate synthetic contrast-enhanced computed tomography (SCECT) from non-contrast CT (NCT) using deep learning (DL) and investigate its role in contouring cardiac substructures. We also aimed to determine its applicability for a retrospective study on the substructure volume-dose relationship for predicting radiation-induced heart disease.

METHODS

We prepared NCT-CECT cardiac scan pairs of 59 patients. Of these, 35, 4, and 20 pairs were used for training, validation, and testing, respectively. We adopted conditional generative adversarial network as a framework to generate SCECT. SCECT was validated in the following three stages: (1) The similarity between SCECT and CECT was evaluated; (2) Manual contouring was performed on SCECT and CECT with sufficient intervals and based on this, the geometric similarity of cardiac substructures was measured between them; (3) The treatment plan was quantitatively analyzed based on the contours of SCECT and CECT.

RESULTS

While the mean values (± standard deviation) of the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure between SCECT and CECT were 20.66 ± 5.29, 21.57 ± 1.85, and 0.77 ± 0.06, those were 23.95 ± 6.98, 20.67 ± 2.34, and 0.76 ± 0.07 between NCT and CECT, respectively. The Dice similarity coefficients and mean surface distance between the contours of SCECT and CECT were 0.81 ± 0.06 and 2.44 ± 0.72, respectively. The dosimetry analysis displayed error rates of 0.13 ± 0.27 Gy and 0.71 ± 1.34% for the mean heart dose and V5Gy, respectively.

CONCLUSION

Our findings displayed the feasibility of SCECT generation from NCT and its potential for cardiac substructure delineation in patients who underwent breast radiation therapy.

Atlas-based auto-segmentation for delineating the heart and cardiac substructures in breast cancer radiation therapy

BACKGROUND

This study aimed to develop and validate an automatic multi-atlas segmentation method for delineating the heart and substructures in breast cancer radiation therapy (RT).

MATERIAL AND METHODS

The atlas database consisted of non-contrast-enhanced planning CT scans from 42 breast cancer patients, each with one manual delineation of the heart and 22 cardiac substructures. Half of the patients were scanned during free-breathing, the rest were scanned during a deep inspiration breath-hold. The auto-segmentation was developed in the MIM software system and validated geometrically and dosimetrically in two steps: The first validation in a small dataset to ensure consistency of the atlas. This was succeeded by a final test where multiple manual delineations in CT scans of 12 breast cancer patients were compared to the auto-segmentation. For geometric evaluation, the dice similarity coefficient (DSC) and the mean surface distance (MSD) were used. For dosimetric evaluation, the RT doses to each substructure in the manual and the automatic delineations were compared.

RESULTS

In the first validation, a high geometric and dosimetric performance between the automatic and manual delineations was observed for all substructures. The final test confirmed a high agreement between the automatic and manual delineations for the heart (DSC = 0.94) and the cardiac chambers (DSC: 0.75-0.86). The difference in MSD between the automatic and manual delineations was low (<4 mm) in all structures. Finally, a high correlation between mean RT doses for the automatic and the manual delineations was observed for the heart and substructures.

CONCLUSIONS

An automatic segmentation tool for delineation of the heart and substructures in breast cancer RT was developed and validated with a high correlation between the automatic and manual delineations. The atlas is pivotal for large-scale evaluations of radiation-associated heart disease.

Heart atlas for retrospective cardiac dosimetry: a multi-institutional study on interobserver contouring variations and their dosimetric impact

PURPOSE

Cardiac effects after breast cancer radiation therapy potentially affect more patients as survival improves. The heart's heterogeneous radiation exposure and composition of functional structures call for establishing individual relationships between structure dose and specific late effects. However, valid dosimetry requires reliable contouring which is challenging for small volumes based on older, lower-quality computed tomography imaging. We developed a heart atlas for robust heart contouring in retrospective epidemiologic studies.

METHODS AND MATERIALS

The atlas defined the complete heart and geometric surrogate volumes for six cardiac structures: aortic valve, pulmonary valve, all deeper structures combined, myocardium, left anterior myocardium, and right anterior myocardium. We collected treatment planning records from 16 patients from 4 hospitals including dose calculations for 3D conformal tangential field radiation therapy for left-sided breast cancer. Six observers each contoured all patients. We assessed spatial contouring agreement and corresponding dosimetric variability.

RESULTS

Contouring agreement for the complete heart was high with a mean Jaccard similarity coefficient (JSC) of 89%, a volume coefficient of variation (CV) of 5.2%, and a mean dose CV of 4.2%. The left (right) anterior myocardium had acceptable agreement with 63% (58%) JSC, 9.8% (11.5%) volume CV, and 11.9% (8.0%) mean dose CV. Dosimetric agreement for the deep structures and aortic valve was good despite higher spatial variation. Low spatial agreement for the pulmonary valve translated to poor dosimetric agreement.

CONCLUSIONS

For the purpose of retrospective dosimetry based on older imaging, geometric surrogate volumes for cardiac organs at risk can yield better contouring agreement than anatomical definitions, but retain limitations for small structures like the pulmonary valve.

AI-Based Radiation Dose Quantification for Estimation of Heart Disease Risk in Breast Cancer Survivors After Radiation Therapy

PURPOSE

To investigate whether the dose planned for cardiac structures is associated with the risk of heart disease (HD) in patients with breast cancer treated with radiation therapy, and whether this association is modified by the presence of coronary artery calcification (CAC).

METHODS AND MATERIALS

Radiation therapy planning computed tomographic (CT) scans and corresponding dose distribution maps of 5561 patients were collected, 5300 patients remained after the exclusion of ineligible patients and duplicates; 1899 patients received their CT scan before 2011, allowing long follow-up. CAC was detected automatically. Using an artificial intelligence-based method, the cardiac structures (heart, cardiac chambers, large arteries, 3 main coronary arteries) were segmented. The planned radiation dose to each structure separately and to the whole heart were determined. Patients were assigned to a low-, medium-, or high-dose group based on the dose to the respective heart structure. Information on HD hospitalization and mortality was obtained for each patient. The association of planned radiation dose to cardiac structures with risk of HD was investigated in patients with and without CAC using Cox proportional hazard analysis in the long follow-up population. Tests for interaction were performed.

RESULTS

After a median follow-up of 96.0 months (interquartile range, 84.2-110.4 months) in the long follow-up group, 135 patients were hospitalized for HD or died of HD. If the dose to a structure increased 1 Gy, the relative HD risk increased by 3% to 11%. The absolute increase in HD risk was substantially higher in patients with CAC (event-rate_low-dose = 14-15 vs event-rate_high-dose = 15-34 per 1000 person-years) than in patients without CAC (event-rate_low-dose = 6-8 vs event-rate_high-dose = 5-17 per 1000 person-years). No interaction between CAC and radiation dose was found.

CONCLUSIONS

Radiation exposure of cardiac structures is associated with increased risk of HD. Automatic segmentation of cardiac structures enables spatially localized dose estimation, which can aid in the prevention of radiation therapy-induced cardiac damage. This could be especially valuable in patients with breast cancer and CAC.

Robustness of deep learning segmentation of cardiac substructures in noncontrast computed tomography for breast cancer radiotherapy

PURPOSE

To develop and evaluate deep learning-based autosegmentation of cardiac substructures from noncontrast planning computed tomography (CT) images in patients undergoing breast cancer radiotherapy and to investigate the algorithm sensitivity to out-of-distribution data such as CT image artifacts.

METHODS

Nine substructures including aortic valve (AV), left anterior descending (LAD), tricuspid valve (TV), mitral valve (MV), pulmonic valve (PV), right atrium (RA), right ventricle (RV), left atrium (LA), and left ventricle (LV) were manually delineated by a radiation oncologist on noncontrast CT images of 129 patients with breast cancer; among them 90 were considered in-distribution data, also named as "clean" data. The image/label pairs of 60 subjects were used to train a 3D deep neural network while the remaining 30 were used for testing. The rest of the 39 patients were considered out-of-distribution ("outlier") data, which were used to test robustness. Random rigid transformations were used to augment the dataset during training. We investigated multiple loss functions, including Dice similarity coefficient (DSC), cross-entropy (CE), Euclidean loss as well as the variation and combinations of these, data augmentation, and network size on overall performance and sensitivity to image artifacts due to infrequent events such as the presence of implanted devices. The predicted label maps were compared to the ground-truth labels via DSC and mean and 90th percentile symmetric surface distance (90th-SSD).

RESULTS

When modified Dice combined with cross-entropy (MD-CE) was used as the loss function, the algorithm achieved a mean DSC = 0.79 ± 0.07 for chambers and  0.39 ± 0.10 for smaller substructures (valves and LAD). The mean and 90th-SSD were 2.7 ± 1.4 and 6.5 ± 2.8 mm for chambers and 4.1 ± 1.7 and 8.6 ± 3.2 mm for smaller substructures. Models with MD-CE, Dice-CE, MD, and weighted CE loss had highest performance, and were statistically similar. Data augmentation did not affect model performances on both clean and outlier data and model robustness was susceptible to network size. For a certain type of outlier data, robustness can be improved via incorporating them into the training process. The execution time for segmenting each patient was on an average 2.1 s.

CONCLUSIONS

A deep neural network provides a fast and accurate segmentation of large cardiac substructures in noncontrast CT images. Model robustness of two types of clinically common outlier data were investigated and potential approaches to improve them were explored. Evaluation of clinical acceptability and integration into clinical workflow are pending.

Cardiac Magnetic Resonance Imaging and Blood Biomarkers for Evaluation of Radiation-Induced Cardiotoxicity in Patients With Breast Cancer: Results of a Phase 2 Clinical Trial

PURPOSE

Radiation therapy (RT) can increase the risk of cardiac events in patients with breast cancer (BC), but biomarkers predicting risk for developing RT-induced cardiac disease are currently lacking. We report results from a prospective clinical trial evaluating early magnetic resonance imaging (MRI) and serum biomarker changes as predictors of cardiac injury and risk of subsequent cardiac events after RT for left-sided disease.

METHODS

Women with node-negative and node-positive (N-/+) left-sided BC were enrolled on 2 institutional review board (IRB)-approved protocols at 2 institutions. MRI was conducted pretreatment (within 1 week of starting radiation), at the end of treatment (last day of treatment ±1 week), and 3 months after the last day of treatment (±2 weeks) to quantify left and right ventricular volumes and function, myocardial fibrosis, and edema. Perfusion changes during regadenoson stress perfusion were also assessed on a subset of patients (n = 28). Serum was collected at the same time points. Whole heart and cardiac substructures were contoured using CT and MRI. Models were constructed using baseline cardiac and clinical risk factors. Associations between MRI-measured changes and dose were evaluated.

RESULTS

Among 51 women enrolled, mean heart dose ranged from 0.80 to 4.7 Gy and mean left ventricular (LV) dose from 1.1 to 8.2 Gy, with mean heart dose 2.0 Gy. T1 time, a marker of fibrosis, and right ventricular (RV) ejection fraction (EF) significantly changed with treatment; these were not dose dependent. T2 (marker of edema) and LV EF did not significantly change. No risk factors were associated with baseline global perfusion. Prior receipt of doxorubicin was marginally associated with decreased myocardial perfusion after RT (P = .059), and mean MHD was not associated with perfusion changes. A significant correlation between baseline IL-6 and mean heart dose (MHD) at the end of RT (ρ 0.44, P = .007) and a strong trend between troponin I and MHD at 3 months post-treatment (ρ 0.33, P = .07) were observed. No other significant correlations were identified.

CONCLUSIONS

In this prospective study of women with left-sided breast cancer treated with contemporary treatment planning, cardiac radiation doses were very low relative to historical doses reported by Darby et al. Although we observed significant changes in T1 and RV EF shortly after RT, these changes were not correlated with whole heart or substructure doses. Serum biomarker analysis of cardiac injury demonstrates an interesting trend between markers and MHD that warrants further investigation.

Application of an automatic segmentation method for evaluating cardiac structure doses received by breast radiotherapy patients

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Atlas-based method for contouring heart substructures on breast radiotherapy CT.

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Excellent agreement between automatic and manual contours for most patients.

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Dice similarity coefficient for LAD was low (0.06) because a narrow, long structure.

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Doses derived from automatic and manual contours agree within observer variability.

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For left breast treatment, right ventricle and LAD dose most senstive to contour shift.

Background and purpose

Quantifying radiation dose to cardiac substructures is important for research on the etiology and prevention of complications following radiotherapy; however, segmentation of substructures is challenging. In this study we demonstrate the application of our atlas-based automatic segmentation method to breast cancer radiotherapy plans for generating radiation doses in support of late effects research.

Material and methods

We applied our segmentation method to contour heart substructures on the computed tomography (CT) images of 70 breast cancer patients who received external photon radiotherapy. Two cardiologists provided manual segmentation of the whole heart (WH), left/right atria, left/right ventricles, and left anterior descending artery (LAD). The automatically contours were compared with manual delineations to evaluate similarity in terms of geometry and dose.

Results

The mean Dice similarity coefficient between manual and automatic segmentations was 0.96 for the WH, 0.65 to 0.82 for the atria and ventricles, and 0.06 for the LAD. The mean average surface distance was 1.2 mm for the WH, 3.4 to 4.1 mm for the atria and ventricles, and 6.4 mm for the LAD. We found the dose to the cardiac substructures based on our automatic segmentation agrees with manual segmentation within expected observer variability. For left breast patients, the mean absolute difference in mean dose was 0.1 Gy for the WH, 0.2 to 0.7 Gy for the atria and ventricles, and 1.8 Gy for the LAD. For right breast patients, these values were 0.0 Gy, 0.1 to 0.4 Gy, and 0.4 Gy, respectively.

Conclusion

Our automatic segmentation method will facilitate the development of radiotherapy prescriptive criteria for mitigating cardiovascular complications.

A novel specific grading standard study of auto-segmentation of organs at risk in thorax: subjective-objective-combined grading standard

BACKGROUND

To develop a novel subjective-objective-combined (SOC) grading standard for auto-segmentation for each organ at risk (OAR) in the thorax.

METHODS

A radiation oncologist manually delineated 13 thoracic OARs from computed tomography (CT) images of 40 patients. OAR auto-segmentation accuracy was graded by five geometric objective indexes, including the Dice similarity coefficient (DSC), the difference of the Euclidean distance between centers of mass (ΔCMD), the difference of volume (ΔV), maximum Hausdorff distance (MHD), and average Hausdorff distance (AHD). The grading results were compared with those of the corresponding geometric indexes obtained by geometric objective methods in the other two centers. OAR auto-segmentation accuracy was also graded by our subjective evaluation standard. These grading results were compared with those of DSC. Based on the subjective evaluation standard and the five geometric indexes, the correspondence between the subjective evaluation level and the geometric index range was established for each OAR.

RESULTS

For ΔCMD, ΔV, and MHD, the grading results of the geometric objective evaluation methods at our center and the other two centers were inconsistent. For DSC and AHD, the grading results of three centers were consistent. Seven OARs' grading results in the subjective evaluation standard were inconsistent with those of DSC. Six OARs' grading results in the subjective evaluation standard were consistent with those of DSC. Finally, we proposed a new evaluation method that combined the subjective evaluation level of those OARs with the range of corresponding DSC to determine the grading standard. If the DSC ranges between the adjacent levels did not overlap, the DSC range was used as the grading standard. Otherwise, the mean value of DSC was used as the grading standard.

CONCLUSIONS

A novel OAR-specific SOC grading standard in thorax was developed. The SOC grading standard provides a possible alternative for evaluation of the auto-segmentation accuracy for thoracic OARs.

Quantifying inter-fraction cardiac substructure displacement during radiotherapy via magnetic resonance imaging guidance

Purpose

Emerging evidence suggests cardiac substructures are highly radiosensitive during radiation therapy for cancer treatment. However, variability in substructure position after tumor localization has not been well characterized. This study quantifies inter-fraction displacement and planning organ at risk volumes (PRVs) of substructures by leveraging the excellent soft tissue contrast of magnetic resonance imaging (MRI).

Methods

Eighteen retrospectively evaluated patients underwent radiotherapy for intrathoracic tumors with a 0.35 T MRI-guided linear accelerator. Imaging was acquired at a 17–25 s breath-hold (resolution 1.5 × 1.5 × 3 mm³). Three to four daily MRIs per patient (n = 71) were rigidly registered to the planning MRI-simulation based on tumor matching. Deep learning or atlas-based segmentation propagated 13 substructures (e.g., chambers, coronary arteries, great vessels) to daily MRIs and were verified by two radiation oncologists. Daily centroid displacements from MRI-simulation were quantified and PRVs were calculated.

Results

Across substructures, inter-fraction displacements for 14% in the left–right, 18% in the anterior-posterior, and 21% of fractions in the superior-inferior were > 5 mm. Due to lack of breath-hold compliance, ~4% of all structures shifted > 10 mm in any axis. For the chambers, median displacements were 1.8, 1.9, and 2.2 mm in the left–right, anterior-posterior, and superior-inferior axis, respectively. Great vessels demonstrated larger displacements (> 3 mm) in the superior-inferior axis (43% of shifts) and were only 25% (left–right) and 29% (anterior-posterior) elsewhere. PRVs from 3 to 5 mm were determined as anisotropic substructure-specific margins.

Conclusions

This exploratory work derived substructure-specific safety margins to ensure highly effective cardiac sparing. Findings require validation in a larger cohort for robust margin derivation and for applications in prospective clinical trials.

Automatic delineation of cardiac substructures using a region-based fully convolutional network

PURPOSE

Radiation dose to specific cardiac substructures, such as the atria and ventricles, has been linked to post-treatment toxicity and has shown to be more predictive of these toxicities than dose to the whole heart. A deep learning-based algorithm for automatic generation of these contours is proposed to aid in either retrospective or prospective dosimetric studies to better understand the relationship between radiation dose and toxicities.

METHODS

The proposed method uses a mask-scoring regional convolutional neural network (RCNN) which consists of five major subnetworks: backbone, regional proposal network (RPN), RCNN head, mask head, and mask-scoring head. Multiscale feature maps are learned from computed tomography (CT) via the backbone network. The RPN utilizes these feature maps to detect the location and region-of-interest (ROI) of all substructures, and the final three subnetworks work in series to extract structural information from these ROIs. The network is trained using 55 patient CT datasets, with 22 patients having contrast scans. Threefold cross validation (CV) is used for evaluation on 45 datasets, and a separate cohort of 10 patients are used for holdout evaluation. The proposed method is compared to a 3D UNet.

RESULTS

The proposed method produces contours that are qualitatively similar to the ground truth contours. Quantitatively, the proposed method achieved average Dice score coefficients (DSCs) for the whole heart, chambers, great vessels, coronary arteries, the valves of the heart of 0.96, 0.94, 0.93, 0.66, and 0.77 respectively, outperforming the 3D UNet, which achieved DSCs of 0.92, 0.87, 0.88, 0.48, and 0.59 for the corresponding substructure groups. Mean surface distances (MSDs) between substructures segmented by the proposed method and the ground truth were <2 mm except for the left anterior descending coronary artery and the mitral and tricuspid valves, and <5 mm for all substructures. When dividing results into noncontrast and contrast datasets, the model performed statistically significantly better in terms of DSC, MSD, centroid mean distance (CMD), and volume difference for the chambers and whole heart with contrast. Notably, the presence of contrast did not statistically significantly affect coronary artery segmentation DSC or MSD. After network training, all substructures and the whole heart can be segmented on new datasets in less than 5 s.

CONCLUSIONS

A deep learning network was trained for automatic delineation of cardiac substructures based on CT alone. The proposed method can be used as a tool to investigate the relationship between cardiac substructure dose and treatment toxicities.

Particle Beam Therapy for Cardiac-Sparing Radiotherapy in Non-Small Cell Lung Cancer

Radiation therapy plays an integral role in the treatment of all stages of non-small cell lung cancer. Survival outcomes are improving, but radiation therapy remains associated with long-term toxicity. Recently, it has become evident that the heart is an important organ at risk for treatment-related morbidity. In this review, we discuss the hypothesis that particle radiation therapy offers superior dosimetry compared with photon-based treatment, and that this comparative advantage translates into clinically meaningful cardiac toxicity reduction with similar local tumor control. We discuss the evidence in non-small cell lung cancer to date, the ongoing prospective trials that may provide additional insight, and the opportunities to optimally integrate particle therapy into future prospective investigation.

Validation of a new open-source method for automatic delineation and dose assessment of the heart and LADCA in breast radiotherapy with simultaneous uncertainty estimation

Radiotherapy has been shown to increase risks of cardiotoxicities for breast cancer patients. Automated delineation approaches are necessary for consistent and efficient assessment of cardiac doses in large, retrospective datasets, while patient-specific estimation of the uncertainty in these doses provides valuable additional data for modelling and understanding risks. In this work, we aim to validate the consistency of our previously described open-source software model for automatic cardiac delineation in the context of dose assessment, relative to manual contouring. We also extend our software to introduce a novel method to automatically quantify the uncertainty in cardiac doses based on expected inter-observer variability (IOV) in contouring. This method was applied to a cohort of 15 left-sided breast cancer patients treated in Denmark using modern tangential radiotherapy techniques. On each image set, the whole heart and left anterior descending coronary artery (LADCA) were contoured by nine independent experts; the range of doses to these nine volumes provided a reference for the dose uncertainties generated from the automatic method. Local and external atlas sets were used to test the method. Results give confidence in the consistency of automatic segmentations, with mean whole heart dose differences for local and external atlas sets of -0.20 ± 0.17 and -0.10 ± 0.14 Gy, respectively. Automatic estimates of uncertainties in doses are similar to those from IOV for both the whole heart and LADCA. Overall, this study confirms that our automated approach can be used to accurately assess cardiac doses, and the proposed method can provide a useful tool in estimating dose uncertainties.

Evaluation of a delineation software for cardiac atlas-based autosegmentation: An example of the use of artificial intelligence in modern radiotherapy

PURPOSE

The primary objective of this work was to implement and evaluate a cardiac atlas-based autosegmentation technique based on the "Workflow Box" software (Mirada Medical, Oxford UK), in order to delineate cardiac substructures according to European Society of Therapeutic Radiation Oncology (ESTRO) guidelines; review and comparison with other cardiac atlas-based autosegmentation algorithms published to date.

MATERIALS AND METHODS

Of an atlas of data set from 20 breast cancer patients' CT scans with recontoured cardiac substructures creation according to the ESTRO guidelines. Performance evaluation on a validation data set consisting of 20 others CT scans acquired in the same treatment position: cardiac substructure were automatically contoured by the Mirada system, using the implemented cardiac atlas, and simultaneously manually contoured by a radiation oncologist. The Dice similarity coefficient was used to evaluate the concordance level between the manual and the automatic segmentations.

RESULTS

Dice similarity coefficient value was 0.95 for the whole heart and 0.80 for the four cardiac chambers. Average Dice similarity coefficient value for the left ventricle walls was 0.50, ranging between 0.34 for the apical wall and 0.70 for the lateral wall. Compared to manual contours, autosegmented substructure volumes were significantly smaller, with the exception of the left ventricle. Coronary artery segmentation was unsuccessful. Performances were overall similar to other published cardiac atlas-based autosegmentation algorithms.

CONCLUSION

The evaluated cardiac atlas-based autosegmentation technique, using the Mirada software, demonstrated acceptable performance for cardiac cavities delineation. However, algorithm improvement is still needed in order to develop efficient and trusted cardiac autosegmentation working tools for daily practice.

Incorporating sensitive cardiac substructure sparing into radiation therapy planning

PURPOSE

Rising evidence suggests that cardiac substructures are highly radiosensitive. However, they are not routinely considered in treatment planning as they are not readily visualized on treatment planning CTs (TPCTs). This work integrated the soft tissue contrast provided by low-field MRIs acquired on an MR-linac via image registration to further enable cardiac substructure sparing on TPCTs.

METHODS

Sixteen upper thoracic patients treated at various breathing states (7 end-exhalation, 7 end-inhalation, 2 free-breathing) on a 0.35T MR-linac were retrospectively evaluated. A hybrid MR/CT atlas and a deep learning three-dimensional (3D) U-Net propagated 13 substructures to TPCTs. Radiation oncologists revised contours using registered MRIs. Clinical treatment plans were re-optimized and evaluated for beam arrangement modifications to reduce substructure doses. Dosimetric assessment included mean and maximum (0.03cc) dose, left ventricular volume receiving 5Gy (LV-V5), and other clinical endpoints. As metrics of plan complexity, total MU and treatment time were evaluated between approaches.

RESULTS

Cardiac sparing plans reduced the mean heart dose (mean reduction 0.7 ± 0.6, range 0.1 to 2.5 Gy). Re-optimized plans reduced left anterior descending artery (LADA) mean and LADA_0.03cc (0.0-63.9% and 0.0 to 17.3 Gy, respectively). LV_0.03cc was reduced by >1.5 Gy for 10 patients while 6 cases had large reductions (>7%) in LV-V5. Left atrial mean dose was equivalent/reduced in all sparing plans (mean reduction 0.9 ± 1.2 Gy). The left main coronary artery was better spared in all cases for mean dose and D_0.03cc . One patient exhibited >10 Gy reduction in D_0.03cc to four substructures. There was no statistical difference in treatment time and MU, or clinical endpoints to the planning target volume, lung, esophagus, or spinal cord after re-optimization. Four patients benefited from new beam arrangements, leading to further dose reductions.

CONCLUSIONS

By introducing 0.35T MRIs acquired on an MR-linac to verify cardiac substructure segmentations for CT-based treatment planning, an opportunity was presented for more effective sparing with limited increase in plan complexity. Validation in a larger cohort with appropriate margins offers potential to reduce radiation-related cardiotoxicities.

Breast size and dose to cardiac substructures in adjuvant three-dimensional conformal radiotherapy compared to tangential intensity modulated radiotherapy

Background

The aim of the study was to quantify planned doses to the heart and specific cardiac substructures in free-breathing adjuvant three-dimensional radiation therapy (3D-CRT) and tangential intensity modulated radiotherapy (t-IMRT) for left-sided node-negative breast cancer, and to assess the differences in planned doses to organs at risk according to patients’ individual anatomy, including breast volume.

Patients and methods

In the study, the whole heart and cardiac substructures were delineated for 60 patients using cardiac atlas. For each patient, 3D-CRT and t-IMRT plans were generated. The prescribed dose was 42.72 Gy in 16 fractions. Patients were divided into groups with small, medium, and large clinical target volume (CTV). Calculated dose distributions were compared amongst the two techniques and the three different groups of CTV.

Results

Mean absorbed dose to the whole heart (MWHD) (1.9 vs. 2.1 Gy, P < 0.005), left anterior descending coronary artery mean dose (8.2 vs. 8.4 Gy, P < 0.005) and left ventricle (LV) mean dose (3.0 vs. 3.2, P < 0.005) were all significantly lower with 3D-CRT technique compared to t-IMRT. Apical (8.5 vs. 9.0, P < 0.005) and anterior LV walls (5.0 vs. 5.4 Gy, P < 0.005) received the highest mean dose (D_mean). MWHD and LV-D_mean increased with increasing CTV size regardless of the technique. Low MWHD values (< 2.5 Gy) were achieved in 44 (73.3%) and 41 (68.3%) patients for 3D-CRT and t-IMRT techniques, correspondingly.

Conclusions

Our study confirms a considerable range of the planned doses within the heart for adjuvant 3D-CRT or t-IMRT in node-negative breast cancer. We observed differences in heart dosimetric metrics between the three groups of CTV size, regardless of the radiotherapy planning technique.

Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer

Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer variation, giving rise to interests in auto-segmentation methods. We evaluated the feasibility of deep learning-based auto-segmentation (DLBAS) in comparison to commercially available atlas-based segmentation solutions (ABAS) for breast cancer radiation therapy. This study used contrast-enhanced planning computed tomography scans from 62 patients with breast cancer who underwent breast-conservation surgery. Contours of target volumes (CTVs), organs, and heart substructures were generated using two commercial ABAS solutions and DLBAS using fully convolutional DenseNet. The accuracy of the segmentation was assessed using 14 test patients using the Dice Similarity Coefficient and Hausdorff Distance referencing the expert contours. A sensitivity analysis was performed using non-contrast planning CT from 14 additional patients. Compared to ABAS, the proposed DLBAS model yielded more consistent results and the highest average Dice Similarity Coefficient values and lowest Hausdorff Distances, especially for CTVs and the substructures of the heart. ABAS showed limited performance in soft-tissue-based regions, such as the esophagus, cardiac arteries, and smaller CTVs. The results of sensitivity analysis between contrast and non-contrast CT test sets showed little difference in the performance of DLBAS and conversely, a large discrepancy for ABAS. The proposed DLBAS algorithm was more consistent and robust in its performance than ABAS across the majority of structures when examining both CTVs and normal organs. DLBAS has great potential to aid a key process in the radiation therapy workflow, helping optimise and reduce the clinical workload.

Advances in Preclinical Research Models of Radiation-Induced Cardiac Toxicity

Radiation therapy (RT) is an important component of cancer therapy, with >50% of cancer patients receiving RT. As the number of cancer survivors increases, the short- and long-term side effects of cancer therapy are of growing concern. Side effects of RT for thoracic tumors, notably cardiac and pulmonary toxicities, can cause morbidity and mortality in long-term cancer survivors. An understanding of the biological pathways and mechanisms involved in normal tissue toxicity from RT will improve future cancer treatments by reducing the risk of long-term side effects. Many of these mechanistic studies are performed in animal models of radiation exposure. In this area of research, the use of small animal image-guided RT with treatment planning systems that allow more accurate dose determination has the potential to revolutionize knowledge of clinically relevant tumor and normal tissue radiobiology. However, there are still a number of challenges to overcome to optimize such radiation delivery, including dose verification and calibration, determination of doses received by adjacent normal tissues that can affect outcomes, and motion management and identifying variation in doses due to animal heterogeneity. In addition, recent studies have begun to determine how animal strain and sex affect normal tissue radiation injuries. This review article discusses the known and potential benefits and caveats of newer technologies and methods used for small animal radiation delivery, as well as how the choice of animal models, including variables such as species, strain, and age, can alter the severity of cardiac radiation toxicities and impact their clinical relevance.

Cardiac substructure segmentation with deep learning for improved cardiac sparing

PURPOSE

Radiation dose to cardiac substructures is related to radiation-induced heart disease. However, substructures are not considered in radiation therapy planning (RTP) due to poor visualization on CT. Therefore, we developed a novel deep learning (DL) pipeline leveraging MRI's soft tissue contrast coupled with CT for state-of-the-art cardiac substructure segmentation requiring a single, non-contrast CT input.

MATERIALS/METHODS

Thirty-two left-sided whole-breast cancer patients underwent cardiac T2 MRI and CT-simulation. A rigid cardiac-confined MR/CT registration enabled ground truth delineations of 12 substructures (chambers, great vessels (GVs), coronary arteries (CAs), etc.). Paired MRI/CT data (25 patients) were placed into separate image channels to train a three-dimensional (3D) neural network using the entire 3D image. Deep supervision and a Dice-weighted multi-class loss function were applied. Results were assessed pre/post augmentation and post-processing (3D conditional random field (CRF)). Results for 11 test CTs (seven unique patients) were compared to ground truth and a multi-atlas method (MA) via Dice similarity coefficient (DSC), mean distance to agreement (MDA), and Wilcoxon signed-ranks tests. Three physicians evaluated clinical acceptance via consensus scoring (5-point scale).

RESULTS

The model stabilized in ~19 h (200 epochs, training error <0.001). Augmentation and CRF increased DSC 5.0 ± 7.9% and 1.2 ± 2.5%, across substructures, respectively. DL provided accurate segmentations for chambers (DSC = 0.88 ± 0.03), GVs (DSC = 0.85 ± 0.03), and pulmonary veins (DSC = 0.77 ± 0.04). Combined DSC for CAs was 0.50 ± 0.14. MDA across substructures was <2.0 mm (GV MDA = 1.24 ± 0.31 mm). No substructures had statistical volume differences (P > 0.05) to ground truth. In four cases, DL yielded left main CA contours, whereas MA segmentation failed, and provided improved consensus scores in 44/60 comparisons to MA. DL provided clinically acceptable segmentations for all graded patients for 3/4 chambers. DL contour generation took ~14 s per patient.

CONCLUSIONS

These promising results suggest DL poses major efficiency and accuracy gains for cardiac substructure segmentation offering high potential for rapid implementation into RTP for improved cardiac sparing.

Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography

Contrast enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for diagnosing cardiovascular diseases non-invasively. It assists the evaluation of the coronary artery patency and provides a comprehensive assessment of structural features of the heart and great vessels. However, physicians are often required to evaluate different cardiac structures and measure their size manually. Such task is very time-consuming and tedious due to the large number of image slices in 3D data. We present a fully automatic method based on a combined multi-atlas and corrective segmentation approach to label the heart and its associated cardiovascular structures. This method also automatically separates other surrounding intrathoracic structures from CTA images. Quantitative assessment of the proposed method is performed on 36 studies with a reference standard obtained from expert manual segmentation of various cardiac structures. Qualitative evaluation is also performed by expert readers to score 120 studies of the automatic segmentation. The quantitative results showed an overall Dice of 0.93, Hausdorff distance of 7.94 mm, and mean surface distance of 1.03 mm between automatically and manually segmented cardiac structures. The visual assessment also attained an excellent score for the automatic segmentation. The average processing time was 2.79 minutes. Our results indicate the proposed automatic framework significantly improves accuracy and computational speed in conventional multi-atlas based approach, and it provides comprehensive and reliable multi-structural segmentation of CTA images that is valuable for clinical application.