Automatic 3D reconstruction of electrophysiology catheters from two-view monoplane C-arm image sequences

Baur, Christoph; Milletarì, Fausto; Belagiannis, Vasileios; Navab, Nassir; Fallavollita, Pascal

doi:10.1007/s11548-015-1325-8

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…The 3D shape‐aware method 42 takes 3D images as input and jointly predicts a binary segmentation map and SDM to train the network. The backbone is a 3D V‐Net architecture 51 that consists of an encoder and decoder with two output branches, one for the segmentation map and the other for the SDM 42 …”

Section: Methodsmentioning

confidence: 99%

“…The 3D shape-aware method 42 takes 3D images as input and jointly predicts a binary segmentation map and SDM to train the network. The backbone is a 3D V-Net architecture 51 that consists of an encoder and decoder with two output branches, one for the segmentation map and the other for the SDM. 42 In addition to the 3D V-Net backbone, the lightweighted SDM head along with the original segmentation head act as two parallel prediction branches.…”

Section: Stage 2: Shape-aware 3d Semantic Segmentationmentioning

confidence: 99%

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Automatic quadriceps and patellae segmentation of MRI with cascaded U²‐Net and SASSNet deep learning model

et al. 2021

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Purpose Automatic muscle segmentation is critical for advancing our understanding of human physiology, biomechanics, and musculoskeletal pathologies, as it allows for timely exploration of large multi‐dimensional image sets. Segmentation models are rarely developed/validated for the pediatric model. As such, autosegmentation is not available to explore how muscle architectural changes during development and how disease/pathology affects the developing musculoskeletal system. Thus, we aimed to develop and validate an end‐to‐end, fully automated, deep learning model for accurate segmentation of the rectus femoris and vastus lateral, medialis, and intermedialis using a pediatric database. Methods We developed a two‐stage cascaded deep learning model in a coarse‐to‐fine manner. In the first stage, the U2‐Net roughly detects the muscle subcompartment region. Then, in the second stage, the shape‐aware 3D semantic segmentation method SASSNet refines the cropped target regions to generate the more finer and accurate segmentation masks. We utilized multifeature image maps in both stages to stabilize performance and validated their use with an ablation study. The second‐stage SASSNet was independently run and evaluated with three different cropped region resolutions: the original image resolution, and images downsampled 2× and 4× (high, mid, and low). The relationship between image resolution and segmentation accuracy was explored. In addition, the patella was included as a comparator to past work. We evaluated segmentation accuracy using leave‐one‐out testing on a database of 3D MR images (0.43 × 0.43 × 2 mm) from 40 pediatric participants (age 15.3 ± 1.9 years, 55.8 ± 11.8 kg, 164.2 ± 7.9 cm, 38F/2 M). Results The mid‐resolution second stage produced the best results for the vastus medialis, rectus femoris, and patella (Dice similarity coefficient = 95.0%, 95.1%, 93.7%), whereas the low‐resolution second stage produced the best results for the vastus lateralis and vastus intermedialis (DSC = 94.5% and 93.7%). In comparing the low‐ to mid‐resolution cases, the vasti intermedialis, vastus medialis, rectus femoris, and patella produced significant differences (p = 0.0015, p = 0.0101, p < 0.0001, p = 0.0003) and the vasti lateralis did not (p = 0.2177). The high‐resolution stage 2 had significantly lower accuracy (1.0 to 4.4 dice percentage points) compared to both the mid‐ and low‐resolution routines (p value ranged from < 0.001 to 0.04). The one exception was the rectus femoris, where there was no difference between the low‐ and high‐resolution cases. The ablation study demonstrated that the multifeature is more reliable than the single feature. Conclusions Our successful implementation of this two‐stage segmentation pipeline provides a critical tool for expanding pediatric muscle physiology and clinical research. With a relatively small and variable dataset, our fully automatic segmentation technique produces accuracies that matched or exceeded the current state of the art. The two‐stage segmentation avoids memor...

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Section: Methodsmentioning

confidence: 99%

Section: Stage 2: Shape-aware 3d Semantic Segmentationmentioning

confidence: 99%

Automatic quadriceps and patellae segmentation of MRI with cascaded U²‐Net and SASSNet deep learning model

et al. 2021

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“…Recent developments enable the 3D reconstruction of organs with many available segmentation tools [ 95 ]. Although segmentation software provides such capabilities, for clinical practice and education these are too complex to be used [ 96 ]. To train medical students and staff to deal with these advanced medical imaging-based reconstructions, an easy-to-use tool accompanied by educational material needs to be developed and tied to the clinical educational field of IC [ 97 ].…”

Section: Ethical Implications Of Ai In Interventional Cardiologymentioning

confidence: 99%

Advancements in artificial intelligence-driven techniques for interventional cardiology

Rudnicka,

Pręgowska,

Glądys

et al. 2024

Cardiol J.

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This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.

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“…Such guide-wire detection could be further used in higher level visualizations, e.g. for 3D guidance using 3D models of the vessels acquired preoperatively with a rotational X-ray angiography [1,3], which is our long-term goal.…”

Section: Introductionmentioning

confidence: 99%

Automatic Guide-Wire Detection for Neurointerventions Using Low-Rank Sparse Matrix Decomposition and Denoising

Zweng

Fallavollita

Demirci

et al. 2015

Augmented Environments for Computer-Assisted Interventions

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In neuro-interventional surgeries, physicians rely on fluoroscopic video sequences to guide tools through the vascular system to the region of interest. Due to the low signal-to-noise ratio of low-dose images and the presence of many line-like structures in the brain, the guide-wire and other tools are difficult to see. In this work we propose an effective method to detect guide-wires in fluoroscopic videos that aims at enhancing the visualization for better intervention guidance. In contrast to prior work, we do not rely on a specific modeling of the catheter (e.g. shape, intensity, etc.), nor on prior statistical learning. Instead, we base our approach on motion cues by making use of recent advances in low-rank and sparse matrix decomposition, which we then combine with denoising. An evaluation on 651 X-ray images from 5 patient shows that our guide-wire tip detection is precise and within clinical tolerance for guide-wire inter-frame motions as high as 6mm.

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Automatic 3D reconstruction of electrophysiology catheters from two-view monoplane C-arm image sequences

Cited by 10 publications

References 19 publications

Automatic quadriceps and patellae segmentation of MRI with cascaded U²‐Net and SASSNet deep learning model

Automatic quadriceps and patellae segmentation of MRI with cascaded U²‐Net and SASSNet deep learning model

Advancements in artificial intelligence-driven techniques for interventional cardiology

Automatic Guide-Wire Detection for Neurointerventions Using Low-Rank Sparse Matrix Decomposition and Denoising

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Automatic 3D reconstruction of electrophysiology catheters from two-view monoplane C-arm image sequences

Cited by 10 publications

References 19 publications

Automatic quadriceps and patellae segmentation of MRI with cascaded U2‐Net and SASSNet deep learning model

Automatic quadriceps and patellae segmentation of MRI with cascaded U2‐Net and SASSNet deep learning model

Advancements in artificial intelligence-driven techniques for interventional cardiology

Automatic Guide-Wire Detection for Neurointerventions Using Low-Rank Sparse Matrix Decomposition and Denoising

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Product

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Automatic quadriceps and patellae segmentation of MRI with cascaded U²‐Net and SASSNet deep learning model

Automatic quadriceps and patellae segmentation of MRI with cascaded U²‐Net and SASSNet deep learning model