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Purpose: For tumor resection, surgeons need to localize the tumor. For this purpose, a magnetic seed can be inserted into the tumor by a radiologist and, during surgery, a magnetic detection probe informs the distance to the seed for localization. In this case, the surgeon still needs to mentally reconstruct the position of the tumor from the probe’s information. The purpose of this study is to develop and assess a method for 3D localization and visualization of the seed, facilitating the localization of the tumor. Methods: We propose a method for 3D localization of the magnetic seed by extending the magnetic detection probe with a tracking-based localization. We attach a position sensor (QR-code or optical marker) to the probe in order to track its 3D pose (respectively, using a head-mounted display with a camera or optical tracker). Following an acquisition protocol, the 3D probe tip and seed position are subsequently obtained by solving a system of equations based on the distances and the 3D probe poses. Results: The method was evaluated with an optical tracking system. An experimental setup using QR-code tracking (resp. using an optical marker) achieves an average of 1.6 mm (resp. 0.8 mm) 3D distance between the localized seed and the ground truth. Using a breast phantom setup, the average 3D distance is 4.7 mm with a QR-code and 2.1 mm with an optical marker. Conclusion: Tracking the magnetic detection probe allows 3D localization of a magnetic seed, which opens doors for augmented reality target visualization during surgery. Such an approach should enhance the perception of the localized region of interest during the intervention, especially for breast tumor resection where magnetic seeds can already be used in the protocol., Computer Graphics and Visualisation, Medical Instruments & Bio-Inspired Technology

Background: X-ray digital subtraction angiography (DSA) is the imaging modality for peri-procedural guidance and treatment evaluation in (neuro-) vascular interventions. Perfusion image construction from DSA, as a means of quantitatively depicting cerebral hemodynamics, has been shown feasible. However, the quantitative property of perfusion DSA has not been well studied. Purpose: To comparatively study the independence of deconvolution-based perfusion DSA with respect to varying injection protocols, as well as its sensitivity to alterations in brain conditions. Methods: We developed a deconvolution-based algorithm to compute perfusion parametric images from DSA, including cerebral blood volume (CBV (Figure presented.)), cerebral blood flow (CBF (Figure presented.)), time to maximum (Tmax), and mean transit time (MTT (Figure presented.)) and applied it to DSA sequences obtained from two swine models. We also extracted the time intensity curve (TIC)-derived parameters, that is, area under the curve (AUC), peak concentration of the curve, and the time to peak (TTP) from these sequences. Deconvolution-based parameters were quantitatively compared to TIC-derived parameters in terms of consistency upon variations in injection profile and time resolution of DSA, as well as sensitivity to alterations of cerebral condition. Results: Comparing to TIC-derived parameters, the standard deviation (SD) of deconvolution-based parameters (normalized with respect to the mean) are two to five times smaller, indicating that they are more consistent across different injection protocols and time resolutions. Upon ischemic stroke induced in a swine model, the sensitivities of deconvolution-based parameters are equal to, if not higher than, those of TIC-derived parameters. Conclusions: In comparison to TIC-derived parameters, deconvolution-based perfusion imaging in DSA shows significantly higher quantitative reliability against variations in injection protocols across different time r, ImPhys/Vos group, ImPhys/Computational Imaging

Extracting the cerebral anterior vessel tree of patients with an intracranial large vessel occlusion (LVO) is relevant to investigate potential biomarkers that can contribute to treatment decision making. The purpose of our work is to develop a method that can achieve this from routinely acquired computed tomography angiography (CTA) and computed tomography perfusion (CTP) images. To this end, we regard the anterior vessel tree as a set of bifurcations and connected centerlines. The method consists of a proximal policy optimization (PPO) based deep reinforcement learning (DRL) approach for tracking centerlines, a convolutional neural network based bifurcation detector, and a breadth-first vessel tree construction approach taking the tracking and bifurcation detection results as input. We experimentally determine the added values of various components of the tracker. Both DRL vessel tracking and CNN bifurcation detection were assessed in a cross validation experiment using 115 subjects. The anterior vessel tree formation was evaluated on an independent test set of 25 subjects, and compared to interobserver variation on a small subset of images. The DRL tracking result achieves a median overlapping rate until the first error (1.8 mm off the reference standard) of 100, [46, 100] % on 8032 vessels over 115 subjects. The bifurcation detector reaches an average recall and precision of 76% and 87% respectively during the vessel tree formation process. The final vessel tree formation achieves a median recall of 68% and precision of 70%, which is in line with the interobserver agreement., ImPhys/Vos group, ImPhys/Computational Imaging, Biomechanical Engineering

Identification and detection of thin-cap fibroatheroma (TCFA) from intravascular optical coherence tomography (IVOCT) images is critical for treatment of coronary heart diseases. Recently, deep learning methods have shown promising successes in TCFA identification. However, most methods usually do not effectively utilize multi-view information or incorporate prior domain knowledge. In this paper, we propose a multi-view contour-constrained transformer network (MVCTN) for TCFA identification in IVOCT images. Inspired by the diagnosis process of cardiologists, we use contour constrained self-attention modules (CCSM) to emphasize features corresponding to salient regions (i.e., vessel walls) in an unsupervised manner and enhance the visual interpretability based on class activation mapping (CAM). Moreover, we exploit transformer modules (TM) to build global-range relations between two views (i.e., polar and Cartesian views) to effectively fuse features at multiple feature scales. Experimental results on a semi-public dataset and an in-house dataset demonstrate that the proposed MVCTN outperforms other single-view and multi-view methods. Lastly, the proposed MVCTN can also provide meaningful visualization for cardiologists via CAM., ImPhys/Vos group, ImPhys/Computational Imaging

Purpose: In minimally invasive spring-assisted craniectomy, surgeons plan the surgery by manually locating the cranial sutures. However, this approach is prone to error. Augmented reality (AR) could be used to visualize the cranial sutures and assist in the surgery planning. The purpose of our work is to develop an AR-based system to visualize cranial sutures, and to assess the accuracy and usability of using AR-based navigation for surgical guidance in minimally invasive spring-assisted craniectomy. Methods: An AR system was developed that consists of an electromagnetic tracking system linked with a Microsoft HoloLens. The system was used to conduct a study with two skull phantoms. For each phantom, five sutures were annotated and visualized on the skull surface. Twelve participants assessed the system. For each participant, model alignment using six anatomical landmarks was performed, followed by the participant delineation of the visualized sutures. At the end, the participants filled a system usability scale (SUS) questionnaire. For evaluation, an independent optical tracking system was used and the delineated sutures were digitized and compared to the CT-annotated sutures. Results: For a total of 120 delineated sutures, the distance of the annotated sutures to the planning reference was 2.4 ± 1.2 mm. The average delineation time per suture was 13 ± 5 s. For the system usability questionnaire, an average SUS score of 73 was obtained. Conclusion: The developed AR-system has good accuracy (average 2.4 mm distance) and could be used in the OR. The system can assist in the pre-planning of minimally invasive craniosynostosis surgeries to locate cranial sutures accurately instead of the traditional approach of manual palpation. Although the conducted phantom study was designed to closely reflect the clinical setup in the OR, further clinical validation of the developed system is needed and will be addressed in a future work., ImPhys/Medical Imaging, ImPhys/Computational Imaging, Biomechanical Engineering

Intracranial vessel perforation is a peri-procedural complication during endovascular therapy (EVT). Prompt recognition is important as its occurrence is strongly associated with unfavorable treatment outcomes. However, perforations can be hard to detect because they are rare, can be subtle, and the interventionalist is working under time pressure and focused on treatment of vessel occlusions. Automatic detection holds potential to improve rapid identification of intracranial vessel perforation. In this work, we present the first study on automated perforation detection and localization on X-ray digital subtraction angiography (DSA) image series. We adapt several state-of-the-art single-frame detectors and further propose temporal modules to learn the progressive dynamics of contrast extravasation. Application-tailored loss function and post-processing techniques are designed. We train and validate various automated methods using two national multi-center datasets (i.e., MR CLEAN Registry and MR CLEAN-NoIV Trial), and one international multi-trial dataset (i.e., the HERMES collaboration). With ten-fold cross-validation, the proposed methods achieve an area under the curve (AUC) of the receiver operating characteristic of 0.93 in terms of series level perforation classification. Perforation localization precision and recall reach 0.83 and 0.70 respectively. Furthermore, we demonstrate that the proposed automatic solutions perform at similar level as an expert radiologist., ImPhys/Medical Imaging, ImPhys/Computational Imaging

Augmented reality (AR) permits the visualization of pre-operative data in the surgical field of view of the surgeon. This requires the alignment of the AR device’s coordinate system with the used navigation/tracking system. We propose a multimodal marker approach to align an AR device with a tracking system: in our implementation, an electromagnetic tracking system (EMTS). The solution makes use of a calibration method which determines the relationship between a 2D pattern detected by an RGB camera and an electromagnetic sensor of the EMTS. This allowed the projection of a 3D skull model on its physical counterpart. This projection was evaluated using a monocular camera and an optical see-through device (HoloLens 2) (https://www.microsoft.com/en-us/hololens/) achieving an accuracy of less than 2.5 mm in the image plane of the HoloLens 2 (HL2). Additionally, 10 volunteers participated in a user study consisting of an alignment task of a pointer with 25 projections viewed through the HL2. The participants achieved a mean error of 2.7 1.3 mm and 2.9 2.9∘ in positional and orientation error. This study showcases the feasibility of the approach, provides an evaluation of the alignment, and finally, discusses its advantages and limitations., ImPhys/Medical Imaging, ImPhys/Computational Imaging

Optical-based navigation systems are widely used in surgical interventions. However, despite their great utility and accuracy, they are expensive and require time and effort to setup for surgeries. Moreover, traditional navigation systems use 2D screens to display instrument positions causing the surgeons to look away from the operative field. Head mounted displays such as the Microsoft HoloLens may provide an attractive alternative for surgical navigation that also permits augmented reality visualization. The HoloLens is equipped with multiple sensors for tracking and scene understanding. Mono and stereo-vision in the HoloLens have been both reported to be used for marker tracking, but no extensive evaluation on accuracy has been done to compare the two approaches. The objective of our work is to investigate the tracking performance of various camera setups in the HoloLens, and to study the effect of the marker size, marker distance from camera, and camera resolution on marker locating accuracy. We also investigate the speed and stability of marker pose for each camera setup. The tracking approaches are evaluated using ArUco markers. Our results show that mono-vision is more accurate in marker locating than stereo-vision when high resolution is used. However, this comes at the expense of higher frame processing time. Alternatively, we propose a combined low-resolution mono-stereo tracking setup that outperforms each tracking approach individually and is comparable to high resolution mono tracking, with a mean translational error of 1.8 ± 0.6mm for 10cm marker size at 50cm distance. We further discuss our findings and their implications for navigation in surgical interventions., ImPhys/Computational Imaging, ImPhys/Medical Imaging

Craniotomy is a procedure where neurosurgeons open the patient's skull to gain direct access to the brain. The craniotomy's position defines the access path from the skull surface to the tumour and, consequently, the healthy brain tissue to be removed to reach the tumour. This is a complex procedure where a neurosurgeon is required to mentally reconstruct spatial relations of important brain structures to avoid removing them as much as possible. We propose a visualisation method using Augmented Reality to assist in the planning of a craniotomy. The goal of this study is to visualise important brain structures aligned with the physical position of the patient and to allow a better perception of the spatial relations of the structures. Additionally, a heat map was developed that is projected on top of the skull to provide a quick overview of the structures between a chosen location on the skull and the tumour. In the experiments, tracking accuracy was assessed, and colour maps were assessed for use in an AR device. Additionally, we conducted a user study amongst neurosurgeons and surgeons from other fields to evaluate the proposed visualisation using a phantom head. Most participants indeed agree that the visualisation can assist in planning a craniotomy and feedback on future improvements towards the clinical scenario was collected, Comp Graphics & Visualisation

Purpose: To present a novel methodical approach to compare visibility of percutaneous needles in ultrasound images. Methods: A motor-driven rotation platform was used to gradually change the needle angle while capturing image data. Data analysis was automated using block-matching-based registration, with a tracking and refinement step. Every 25 frames, a Hough transform was used to improve needle alignments after large rotations. The method was demonstrated by comparing three commercial needles (14G radiofrequency ablation, RFA; 18G Trocar; 22G Chiba) and six prototype needles with different sizes, materials, and surface conditions (polished, sand-blasted, and kerfed), within polyvinyl alcohol phantom tissue and ex vivo bovine liver models. For each needle and angle, a contrast-to-noise ratio (CNR) was determined to quantify visibility. CNR values are presented as a function of needle type and insertion angle. In addition, the normalized area under the (CNR-angle) curve was used as a summary metric to compare needles. Results: In phantom tissue, the first kerfed needle design had the largest normalized area of visibility and the polished 1 mm diameter stainless steel needle the smallest (0.704 ± 0.199 vs. 0.154 ± 0.027, p < 0.01). In the ex vivo model, the second kerfed needle design had the largest normalized area of visibility, and the sand-blasted stainless steel needle the smallest (0.470 ± 0.190 vs. 0.127 ± 0.047, p < 0.001). As expected, the analysis showed needle visibility peaks at orthogonal insertion angles. For acute or obtuse angles, needle visibility was similar or reduced. Overall, the variability in needle visibility was considerably higher in livers. Conclusion: The best overall visibility was found with kerfed needles and the commercial RFA needle. The presented methodical approach to quantify ultrasound visibility allows comparisons of (echogenic) needles, as well as other technological innovations aiming to improve ultrasound visibility o, Medical Instruments & Bio-Inspired Technology

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter- and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.

Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CTA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CTA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (1) (semi-)automatically detect and quantify stenosis on CTA, in comparison with quantitative coronary angiography (QCA) and CTA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CTA, in comparison with expert’s manual annotation. A database consisting of 48 multicenter multivendor cardiac CTA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/.

Accurate alignment of intra-operative X-ray coronary angiography (XA) and pre-operative cardiac CT angiography (CTA) may improve procedural success rates of minimally invasive coronary interventions for patients with chronic total occlusions. It was previously shown that incorporating patient specific coronary motion extracted from 4D CTA increases the robustness of the alignment. However, pre-operative CTA is often acquired with gating at end-diastole, in which case patient specific motion is not available. For such cases, we investigate the possibility of using population based coronary motion models to provide constraints for the 2D+t/3D registration. We propose a methodology for building statistical motion models of the coronary arteries from a training population of 4D CTA datasets. We compare the 2D+t/3D registration performance of the proposed statistical models with other motion estimates, including the patient specific motion extracted from 4D CTA, the mean motion of a population, the predicted motion based on the cardiac shape. The coronary motion models, constructed on a training set of 150 patients, had a generalization accuracy of 1mm root mean square point-to-point distance. Their 2D+t/3D registration accuracy on one cardiac cycle of 12 monoplane XA sequences was similar to, if not better than, the 4D CTA based motion, irrespective of which respiratory model and which feature based 2D/3D distance metric was used. The resulting model based coronary motion estimate showed good applicability for registration of a subsequent cardiac cycle.

Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CIA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CIA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (I) (semi-)automatically detect and quantify stenosis on CIA, in comparison with quantitative coronary angiography (QCA) and CIA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CIA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CIA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/.

We propose a minimum cost path approach to track the centerlines of the internal and external carotid arteries in multispectral MR data. User interaction is limited to the annotation of three seed points. The cost image is based on both a measure of vessel medialness and lumen intensity similarity in two MRA image sequences: Black Blood MRA and Phase Contrast MRA. After intensity inhomogeneity correction and noise reduction, the two images are aligned using affine registration. The two parameters that control the contrast of the cost image were determined in an optimization experiment on 40 training datasets. Experiments on the training datasets also showed that a cost image composed of a combination of gradient-based medialness and lumen intensity similarity increases the tracking accuracy compared to using only one of the constituents. Furthermore, centerline tracking using both MRA sequences outperformed tracking using only one of these MRA images. An independent test set of 152 images from 38 patients served to validate the technique. The centerlines of 148 images were successfully extracted using the parameters optimized on the training sets. The average mean distance to the reference standard, manually annotated centerlines, was 0.98 mm, which is comparable to the in-plane resolution. This indicates that the proposed method has a high potential to replace the manual centerline annotation., Image Science and Technology, Applied Sciences

Cardiac magnetic resonance perfusion imaging (CMR) and computed tomography angiography (CTA) are widely used to assess heart disease. CMR is used to measure the global and regional myocardial function and to evaluate the presence of ischemia; CTA is used for diagnosing coronary artery disease, such as coronary stenoses. Nowadays, the hemodynamic significance of coronary artery stenoses is determined subjectively by combining information on myocardial function with assumptions on coronary artery territories. As the anatomy of coronary arteries varies greatly between individuals, we developed a patient-specific tool for relating CTA and perfusion CMR data. The anatomical and functional information extracted from CTA and CMR data are combined into a single frame of reference. Our graphical user interface provides various options for visualization. In addition to the standard perfusion Bull’s Eye Plot (BEP), it is possible to overlay a 2D projection of the coronary tree on the BEP, to add a 3D coronary tree model and to add a 3D heart model. The perfusion BEP, the 3D-models and the CTA data are also interactively linked. Using the CMR and CTA data of 14 patients, our tool directly established a spatial correspondence between diseased coronary artery segments and myocardial regions with abnormal perfusion. The location of coronary stenoses and perfusion abnormalities were visualized jointly in 3D, thereby facilitating the study of the relationship between the anatomic causes of a blocked artery and the physiological effects on the myocardial perfusion. This tool is expected to improve diagnosis and therapy planning of early-stage coronary artery disease., Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

This paper presents a level set based method for segmenting the outer vessel wall and plaque components of the carotid artery in CTA. The method employs a GentleBoost classification framework that classifies pixels as calcified region or not, and inside or outside the vessel wall. The combined result of both classifications is used to construct a speed function for level set based segmentation of the outer vessel wall; the segmented lumen is used to initialize the level set. The method has been optimized on 20 datasets and evaluated on 80 datasets for which manually annotated data was available as reference. The average Dice similarity of the outer vessel wall segmentation was 92%, which compares favorably to previous methods., Imaging Science and Technology, Applied Sciences

Three-dimensional patient specific bone models are required in a range of medical applications, such as pre-operative surgery planning and improved guidance during surgery, modeling and simulation, and in vivo bone motion tracking. Shape reconstruction from a small number of X-ray images is desired as it lowers both the acquisition costs and the radiation dose compared to CT. We propose a method for pose estimation and shape reconstruction of 3D bone surfaces from two (or more) calibrated X-ray images using a statistical shape model (SSM). User interaction is limited to manual initialization of the mean shape. The proposed method combines a 3D distance based objective function with automatic edge selection on a Canny edge map. Landmark-edge correspondences are weighted based on the orientation difference of the projected silhouette and the corresponding image edge. The method was evaluated by rigid pose estimation of ground truth shapes as well as 3D shape estimation using a SSM of the whole femur, from stereo cadaver X-rays, in vivo biplane fluoroscopy image-pairs, and an in vivo biplane fluoroscopic sequence. Ground truth shapes for all experiments were available in the form of CT segmentations. Rigid registration of the ground truth shape to the biplane fluoroscopy achieved sub-millimeter accuracy (0.68 mm) measured as root mean squared (RMS) point-to-surface (P2S) distance. The non-rigid reconstruction from the biplane fluoroscopy using the SSM also showed promising results (1.68 mm RMS P2S). A feasibility study on one fluoroscopic time series illustrates the potential of the method for motion and shape estimation from fluoroscopic sequences with minimal user interaction., Three-dimensional patient specific bone models are required in a range of medical applications, such as pre-operative surgery planning and improved guidance during surgery, modeling and simulation, and in vivo bone motion tracking. Shape reconstruction from a small number of X-ray images is desired as it lowers both the acquisition costs and the radiation dose compared to CT. We propose a method for pose estimation and shape reconstruction of 3D bone surfaces from two (or more) calibrated X-ray images using a statistical shape model (SSM). User interaction is limited to manual initialization of the mean shape. The proposed method combines a 3D distance based objective function with automatic edge selection on a Canny edge map. Landmark-edge correspondences are weighted based on the orientation difference of the projected silhouette and the corresponding image edge. The method was evaluated by rigid pose estimation of ground truth shapes as well as 3D shape estimation using a SSM of the whole femur, from stereo cadaver X-rays, in vivo biplane fluoroscopy image-pairs, and an in vivo biplane fluoroscopic sequence. Ground truth shapes for all experiments were available in the form of CT segmentations. Rigid registration of the ground truth shape to the biplane fluoroscopy achieved sub-millimeter accuracy (0.68mm) measured as root mean squared (RMS) point-to-surface (P2S) distance. The non-rigid reconstruction from the biplane fluoroscopy using the SSM also showed promising results (1.68mm RMS P2S). A feasibility study on one fluoroscopic time series illustrates the potential of the method for motion and shape estimation from fluoroscopic sequences with minimal user interaction.

Imaging Science and Technology, Applied Sciences

Imaging Science & Technology, Applied Sciences

Efficiently obtaining a reliable coronary artery centerline from computed tomography angiography data is relevant in clinical practice. Whereas numerous methods have been presented for this purpose, up to now no standardized evaluation methodology has been published to reliably evaluate and compare the performance of the existing or newly developed coronary artery centerline extraction algorithms. This paper describes a standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms. The contribution of this work is fourfold: (1) a method is described to create a consensus centerline with multiple observers, (2) well-defined measures are presented for the evaluation of coronary artery centerline extraction algorithms, (3) a database containing 32 cardiac CTA datasets with corresponding reference standard is described and made available, and (4) 13 coronary artery centerline extraction algorithms, implemented by different research groups, are quantitatively evaluated and compared. The presented evaluation framework is made available to the medical imaging community for benchmarking existing or newly developed coronary centerline extraction algorithms.

Electrical Engineering, Mathematics and Computer Science

AbstractBackgroundAblation zone segmentation in contrast‐enhanced computed tomography (CECT) images enables the quantitative assessment of treatment success in the ablation of liver lesions. However, fully automatic liver ablation zone segmentation in CT images still remains challenging, such as low accuracy and time‐consuming manual refinement of the incorrect regions.PurposeTherefore, in this study, we developed a semi‐automatic technique to address the remaining drawbacks and improve the accuracy of the liver ablation zone segmentation in the CT images.MethodsOur approach uses a combination of a CNN‐based automatic segmentation method and an interactive CNN‐based segmentation method. First, automatic segmentation is applied for coarse ablation zone segmentation in the whole CT image. Human experts then visually validate the segmentation results. If there are errors in the coarse segmentation, local corrections can be performed on each slice via an interactive CNN‐based segmentation method. The models were trained and the proposed method was evaluated using two internal datasets of post‐interventional CECT images ( = 22, = 145; 62 patients in total) and then further tested using an external benchmark dataset ( = 12; 10 patients).ResultsTo evaluate the accuracy of the proposed approach, we used Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), Hausdorff distance (HD), and volume difference (VD). The quantitative evaluation results show that the proposed approach obtained mean DSC, ASSD, HD, and