Multilevel segmentation of intracranial aneurysms in CT angiography images

Wang, Yan; Zhang, Yue; Navarro, Laurent; Eker, Omer; Corredor-Jerez, Ricardo; Chen, Yu; Zhu, Yuemin; Courbebaisse, Guy

doi:10.1118/1.4943375

Cited by 21 publications

(12 citation statements)

References 46 publications

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“…Manual segmentation was used as the ground-truth to evaluate the automatic segmentation 32,34,67 , where the Dice value is commonly used for measuring the overlap between manual and automatic segmentation. A MevisLab-based tool was implemented to help our readers draw the manual contours.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Fully automatic segmentation of 4D MRI for cardiac functional measurements

et al. 2018

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Purpose: Segmentation of cardiac medical images, an important step in measuring cardiac function, is usually performed either manually or semi-automatically. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV) as well as the myocardium of 3D magnetic resonance (MR) images throughout the entire cardiac cycle (4D), remains challenging. This study proposes a deformable-based segmentation methodology for efficiently segmenting 4D (3D+t) cardiac MR images. Methods: The proposed methodology first used the Hough transform and the local Gaussian distribution method (LGD) to segment the LV endocardial contours from cardiac MR images. Following that a novel level set-based shape prior method was applied to generate the LV epicardial contours and the RV boundary. Results: This automatic image segmentation approach has been applied to studies on 17 subjects. The results demonstrated that the proposed method was efficient compared to manual segmentation, achieving a segmentation accuracy with average Dice values of 88.62±5.47%, 87.35±7.26%, and 82.63±6.22% for the LV endocardial, LV epicardial and RV contours, respectively. Conclusions: We have presented a method for accurate LV and RV segmentation. Compared to three existing methods, the proposed method can successfully segment the LV and yields the highest Dice value. This makes it an option for clinical assessment of the volume, size and thickness of the ventricles.

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Section: Experiments and Resultsmentioning

confidence: 99%

Fully automatic segmentation of 4D MRI for cardiac functional measurements

et al. 2018

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“…The LSSPM was used to segment the 2D RTMRI images [31]. Specifically, the first frame of each imaging series was manually contoured while subsequent frames were processed with the LSSPM [31].…”

Section: Image Segmentationmentioning

confidence: 99%

Left ventricular geometry during unloading and the end-systolic pressure volume relationship: Measurement with a modified real-time MRI-based method in normal sheep

et al. 2020

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The left ventricular (LV) end-systolic (ES) pressure volume relationship (ESPVR) is the cornerstone of systolic LV function analysis. We describe a 2D real-time (RT) MRI-based method (RTPVR) with separate software tools for 1) semi-automatic level set-based shape prior method (LSSPM) of the LV, 2) generation of synchronized pressure area loops and 3) calculation of the ESPVR. We used the RTPVR method to measure ventricular geometry, ES pressure area relationship (ESPAR) and ESPVR during vena cava occlusion (VCO) in normal sheep. 14 adult sheep were anesthetized and underwent measurement of LV systolic function. Ten of the 14 sheep underwent RTMRI and eight of the 14 underwent measurement with conductance catheter; 4 had both RTMRI and conductance measurements. 2D cross sectional RTMRI were performed at apex, mid-ventricle and base levels during separate VCOs. The Dice similarity coefficient was used to compare LSSPM and manual image segmentation and thus determine LSSPM accuracy. LV cross-sectional area, major and minor axis length, axis ratio, major axis orientation angle and ESPAR were measured at each LV level. ESPVR was calculated with a trapezoidal rule. The Dice similarity coefficient between LSSPM and manual segmentation by two readers was 87.31±2.51% and 88.13 ±3.43%. All cross sections became more elliptical during VCO. The major axis orientation shifted during VCO but remained in the septo-lateral direction. LV chamber obliteration at the apical level occurred during VCO in 7 of 10 sheep that underwent RTMRI. ESPAR was non-linear at all levels. Finally, ESPVR was non-linear because of apical collapse. ESPVR measured by conductance catheter (E ES,Index = 2.23±0.66 mmHg/ml/m 2) and RT (E ES,Index = 2.31±0.31 mmHg/ml/m 2) was not significantly different. LSSPM segmentation of 2D RT MRI images is accurate and allows calculation of LV geometry, ESPAR and ESPVR during

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“…The LSSM was used to segment the 2D RTMRI images [24]. Specifically, the first frame of each imaging series was manually contoured while subsequent frames were processed with the LSSM [24].…”

Section: Image Segmentationmentioning

confidence: 99%

Left Ventricular Chamber Shape During Vena Caval Occlusion: Improved MRI-based Measurement of the End-Systolic Pressure-Volume Relationship in Normal Sheep

Wang²,

Rojas

et al. 2019

Preprint

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The left ventricular (LV) end-systolic (ES) pressure volume relationship (ESPVR) is the cornerstone of systolic LV function analysis. We describe a 2D real-time (RT) MRI-based method (RTPVR) with separate software tools for 1) semi-automatic level set segmentation (LSSM) of the LV, 2) generation of synchronized pressure area loops and 3) calculation of the ESPVR. We used the RTPVR method to measure ventricular geometry, ESPAR and ESPVR during vena cava occlusion (VCO) in normal sheep. 10 adult sheep were anesthetized and 2D cross sectional RTMRI performed at apex, midventricle and base levels during separate VCOs. The Dice similarity coefficient was used to compare LSSM and manual image segmentation and thus determine LSSM accuracy. LV crosssectional area, major and minor axis length, axis ratio, major axis orientation angle and ESPAR were measured at each LV level. ESPVR was calculated with a modified Simpson's rule method. The Dice similarity coefficient between LSSM compared and manual segmentation by two readers was 87.31±2.51% and 88.13±3.43% respectively. All cross sections became more elliptical during VCO. The major axis orientation shifted during VCO but remained in the septo-lateral direction. LV chamber obliteration at the apical level occurred during VCO in 7 of 10 sheep. ESPAR was non-linear at all levels. Finally, ESPVR was non-linear because of apical collapse.LSSM segmentation of 2D RT MRI images is accurate and allows calculation of LV geometry, ESPAR and ESPVR during VCO. In the future, RTPVR will facilitate determination of regional systolic material parameters underlying ESPVR. End-systolic Pressure Volume Relationship by Real-time MRI 3 Word Count: 245 words End-systolic Pressure Volume Relationship by Real-time MRI

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Multilevel segmentation of intracranial aneurysms in CT angiography images

Cited by 21 publications

References 46 publications

Fully automatic segmentation of 4D MRI for cardiac functional measurements

Fully automatic segmentation of 4D MRI for cardiac functional measurements

Left ventricular geometry during unloading and the end-systolic pressure volume relationship: Measurement with a modified real-time MRI-based method in normal sheep

Left Ventricular Chamber Shape During Vena Caval Occlusion: Improved MRI-based Measurement of the End-Systolic Pressure-Volume Relationship in Normal Sheep

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