Automated segmentation of the atrial region and fossa ovalis towards computer-aided planning of inter-atrial wall interventions

Morais, Pedro; Vilaça, João L.; Queirós, Sandro; Marchi, Alberto; Bourier, Félix; Deisenhofer, Isabel; D'hooge, Jan; Tavares, João Manuel R. S.

doi:10.1016/j.cmpb.2018.04.014

Cited by 3 publications

(5 citation statements)

References 38 publications

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“…Overall, thanks to the obtained results in this preliminary study, it was possible to corroborate the robustness of the previously described 3D blind-ended model implemented in the BEAS framework, reinforcing its potential to accurately represent the LAA anatomy. Moreover, and as already discussed in [14,15], it was also possible to demonstrate the potential of the BEAS framework to segment different cardiac structures in CT images.…”

Section: Discussionmentioning

confidence: 69%

3D segmentation of the left atrial appendage in computed tomography for planning of transcatheter occlusion

Morais

Nelles

Schrickel

et al. 2022

Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling

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Anatomical evaluation of the Left Atrial Appendage (LAA) is paramount in patients undergoing percutaneous LAA occlusion as an alternative to oral anticoagulation. Three-dimension assessment and visualization of the LAA is valuable to understand the anatomy of this very variable anatomic structure aiding device-selection andsizing. In this context computed tomography (CT) has been successfully used, however, the clinical practice is mainly based on manual analysis, making it tedious and observer dependent.In this work, we propose a methodology to perform a fast 3D segmentation of the LAA in CT based on our curvilinear B-spline Explicit Active Surface framework. Starting from a manually defined centerline, a blind-ended model is initialized and refined through: (i) a fast contour growing using a global formulation and (ii) a contour refinement based on local energies. Its feasibility in terms of segmentation error and clinical metrics (the diameter of the landing zone [LZ] and the LAA depth) is compared with manual references.The proposed methodology was tested in a database with 15 patients. It registered a point-to-surface error of 0.80±0.24mm, a Dice value of 88.08±3.57%, and a Hausdorff distance (95th percentile) of 2.55±1.19mm. It was found a bias error of -0.36mm (p>0.05) and -0.55mm (p<0.05) for the LZ and LAA depth, with narrow limits-of-agreement for both cases (LZ: [-1.98,1.27]mm; LAA depth: [-2.20,1.10]mm).The proposed methodology showed its high feasibility and performance in this preliminary study, reinforcing its potential for the planning of LAA occlusion.

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Section: Discussionmentioning

confidence: 69%

3D segmentation of the left atrial appendage in computed tomography for planning of transcatheter occlusion

Morais

Nelles

Schrickel

et al. 2022

Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling

Self Cite

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“…However, the described pipeline is only an initial proof-of-concept and it still presents some drawbacks, namely manual interaction is mandatory in all stages, making the configuration of the setup extremely time-consuming. Recently, our team has presented different methodologies to automate the this framework: 1) automatic segmentation of the atrial region in CT [4,15]; 2) automatic identification of the FO in CT [4]; and 3) automatic segmentation of the LA [16]. As such, the entire planning can be performed quickly (2-3 min, [4]) and the fusion stage can be quickly performed by aligning the segmented models.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, our team has presented different methodologies to automate the this framework: 1) automatic segmentation of the atrial region in CT [4,15]; 2) automatic identification of the FO in CT [4]; and 3) automatic segmentation of the LA [16]. As such, the entire planning can be performed quickly (2-3 min, [4]) and the fusion stage can be quickly performed by aligning the segmented models. Although the current automated modules are not integrated into this framework, such options are expected in a future release.…”

Section: Discussionmentioning

confidence: 99%

“…Step B: Based on the 3D contours from (A), the FO was manually identified. For that, we detected the thinnest region, as described in [4]. Then, the optimal puncture location was marked and saved in stl format.…”

Section: )mentioning

confidence: 99%

“…Regarding the planning techniques, a small number of studies were presented, focusing on biomechanical simulation of the intervention [3] or automated identification of relevant landmarks (e.g. fossa ovalis position) [4], making the planning stage faster and more reproducible. Regarding the intraoperative guidance, several researchers explored the potential use of novel imaging modalities (beyond the traditional ones, magnetic resonance imaging -MRI, and intracardiac echocardiography) for TSP [1,6].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Interventional Guidance Framework for Transseptal Puncture in Left Atrial Interventions

Morais

Vilaça

Queirós

et al. 2018

Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation

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Access to the left atrium is required for several percutaneous cardiac interventions. In these procedures, the inter-atrial septal wall is punctured using a catheter inserted in the right atrium via the venous system under image guidance. Although this approach (termed transseptal puncture-TSP) is performed daily, complications are common. Moreover, the exact location at which the septum needs to be traversed is determined entirely based on the interventionist's experience, which is sub-optimal. In this work, we present a novel concept for the development of an interventional guidance framework for TSP. The pre-procedural planning stage is fused with 3D intra-procedural images (echocardiography) using manually defined landmarks, transferring the relevant anatomical landmarks to the interventional space and enhancing the echocardiographic images. In addition, electromagnetic sensors are attached to the surgical instruments, tracking them and allowing the inclusion of their spatial position in the enhanced intra-procedural world. Two patient-specific atrial phantom models were used to evaluate this framework. One operator performed the planning, calibrated the setup and performed the puncture. To assess the framework's accuracy, a metallic landmark was positioned in the punctured location and compared with the ideal one. The intervention was possible in both models, but in one case positioning of the landmark failed. An error of approximately of 6 mm was registered for the successful case. Technical characteristics of the framework showed an acceptable performance, with a frame rate ~5 frames/sec. The manual calibration setup required ~60 min. This study presented a proof-of-concept for an interventional guidance framework for TSP. However, a more automated solution and further studies to assess its accuracy are required.

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Semi-supervised segmentation of cardiac chambers from LGE-CMR using feature consistency awareness

Wang,

Huang,

et al. 2024

BMC Cardiovasc Disord

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Background Late gadolinium enhancement cardiac magnetic resonance imaging (LGE-CMR) is a valuable cardiovascular imaging technique. Segmentation of cardiac chambers from LGE-CMR is a fundamental step in electrophysiological modeling and cardiovascular disease diagnosis. Deep learning methods have demonstrated extremely promising performance. However, excellent performance often depended on a large amount of finely annotated data. The purpose of this manuscript was to develop a semi-supervised segmentation method to use unlabeled data to improve model performance. Methods This manuscript proposed a semi-supervised network that integrates triple-consistency constraints (data-level, task-level, and feature-level) for cardiac chambers segmentation from LGE-CMR. Specifically, we designed a network that integrated segmentation and edge prediction tasks based on the mean teacher architecture. This addressed the problem of ignoring some challenging regions because of excluding low-confidence regions of previous research. We also applied a voxel-level contrastive learning strategy to achieve feature-level consistency, helping the model pay attention to the consistency between features overlooked in previous research. Results In terms of the Dice, Jaccard, Average Surface Distance (ASD), and 95% Hausdorff Distance (95HD) metrics, for the atrial segmentation dataset, the proposed method achieved scores of 88.34%, 79.30%, 7.92, and 2.02 when trained with 10% labeled data, and 90.70%, 83.09%, 6.41, and 1.72 when trained with 20% labeled data. For the ventricular segmentation task, the results were 87.22%, 77.95%, 2.27, and 0.61 with 10% labeled data, and 88.99%, 80.45%, 1.87, and 0.51 with 20% labeled data, respectively. Conclusion Experiments demonstrated that our method outperforms previous semi-supervised methods, showing the potential of the proposed network for semi-supervised segmentation problems.

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Automated segmentation of the atrial region and fossa ovalis towards computer-aided planning of inter-atrial wall interventions

Abstract: Hence, the proposed method proved to be feasible to automatically segment the anatomical models for the planning of IAS wall interventions, making it exceptionally attractive for use in the clinical practice.

Cited by 3 publications

References 38 publications

3D segmentation of the left atrial appendage in computed tomography for planning of transcatheter occlusion

3D segmentation of the left atrial appendage in computed tomography for planning of transcatheter occlusion

A Novel Interventional Guidance Framework for Transseptal Puncture in Left Atrial Interventions

Semi-supervised segmentation of cardiac chambers from LGE-CMR using feature consistency awareness

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