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During percutaneous coronary intervention, the guiding catheter plays an important role. Tracking the catheter tip placed at the coronary ostium in the X-ray fluoroscopy sequence can obtain image displacement information caused by the heart beating, which can help dynamic coronary roadmap overlap on X-ray fluoroscopy images. Due to a low exposure dose, the X-ray fluoroscopy is noisy and low contrast, which causes some difficulties in tracking. In this paper, we developed a new catheter tip tracking framework. First, a lightweight efficient catheter tip segmentation network is proposed and boosted by a self-distillation training mechanism. Then, the Bayesian filtering post-processing method is used to consider the sequence information to refine the single image segmentation results. By separating the segmentation results into several groups based on connectivity, our framework can track multiple catheter tips. The proposed tracking framework is validated on a clinical X-ray sequence dataset., (© 2023 John Wiley & Sons Ltd.)

Video-assisted thoracoscopic surgery (VATS) is a minimally invasive surgical technique for the diagnosis and treatment of early-stage lung cancer. During VATS, large lung deformation occurs as a result of a change of patient position and a pneumothorax (lung deflation), which hinders the intraoperative localization of pulmonary nodules. Modeling lung deformation during VATS for surgical navigation is desirable, but the mechanisms causing such deformation are yet not well-understood. In this study, we estimate, quantify and analyze the lung deformation occurring after a change of patient position during VATS. We used deformable image registration to estimate the lung deformation between a preoperative CT (in supine position) and an intraoperative CBCT (in lateral decubitus position) of six VATS clinical cases. We accounted for sliding motion between lobes and against the thoracic wall and obtained consistently low average target registration errors (under 1 mm). We observed large lung displacement (up to 40 mm); considerable sliding motion between lobes and against the thoracic wall (up to 30 mm); and localized volume changes indicating deformation. These findings demonstrate the complexity of the change of patient position phenomenon, which should necessarily be taken into account to model lung deformation for intraoperative guidance during VATS.

Objective. Plan-of-the-day (PoD) adaptive radiation therapy (ART) is based on a library of treatment plans, among which, at each treatment fraction, the PoD is selected using daily images. However, this strategy is limited by PoD selection uncertainties. This work aimed to propose and evaluate a workflow to automatically and quantitatively identify the PoD for cervix cancer ART based on daily CBCT images. Approach. The quantification was based on the segmentation of the main structures of interest in the CBCT images (clinical target volume [CTV], rectum, bladder, and bowel bag) using a deep learning model. Then, the PoD was selected from the treatment plan library according to the geometrical coverage of the CTV. For the evaluation, the resulting PoD was compared to the one obtained considering reference CBCT delineations. Main results. In experiments on a database of 23 patients with 272 CBCT images, the proposed method obtained an agreement between the reference PoD and the automatically identified PoD for 91.5% of treatment fractions (99.6% when considering a 5% margin on CTV coverage). Significance. The proposed automatic workflow automatically selected PoD for ART using deep-learning methods. The results showed the ability of the proposed process to identify the optimal PoD in a treatment plan library., (© 2022 Institute of Physics and Engineering in Medicine.)

High-intensity focused ultrasound (HIFU) is a promising method used to treat cardiac arrhythmias, as it can induce lesions at a distance throughout myocardium thickness. Numerical modeling is commonly used for ultrasound probe development and optimization of HIFU treatment strategies. This study was aimed at describing a numerical method to simulate HIFU thermal ablation in elastic and mobile heart models. The ultrasound pressure field is computed on a 3-D orthonormal grid using the Rayleigh integral method, and the attenuation is calculated step by step between cells. The temperature distribution is obtained by resolution of the bioheat transfer equation on a 3-D non-orthogonally structured curvilinear grid using the finite-volume method. The simulation method is applied on two regions of the heart (atrioventricular node and ventricular apex) to compare the thermal effects of HIFU ablation depending on deformation, motion type and amplitude. The atrioventricular node requires longer sonication than the ventricular apex to reach the same lesion volume. Motion considerably influences treatment duration, lesion shape and distribution in cardiac HIFU treatment. These results emphasize the importance of considering local motion and deformation in numerical studies to define efficient and accurate treatment strategies., Competing Interests: Conflict of interest disclosure The authors declare no conflicts of interest., (Copyright © 2022 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

In this article, a solution to detect the change of behaviour of the elderly person based on the person's activities of daily living is proposed. This work is based on the hypothesis that the person attaches importance to a rhythmic sequence of days and activities per day. The day of the elderly person is described by a succession of activities, and each activity is associated to a posture (lying down, sitting, standing, absent). Postures are estimated from image analysis measured by thermal or depth cameras in order to preserve the anonymity of the person. The change in posture succession is calculated using the minimum edit distance with respect to the routine day. The number of permutations/inversions reflects the change in the person's behaviour. The method was tested on two elderly persons recorded by thermal and depth cameras during 85 days in a retirement home. It is shown that for a person with a life change behaviour, the average number of permutations and interquartile range, before and after changes, are 41 [28], [48] and 57 [55-62] respectively compared to the learned routine day. The Wilcoxon test confirmed the significant difference between these two periods.Clinical Relevance- Monitoring the daily routine provides indicators for detecting changes in the behaviour of an elderly person.

In this paper, we propose a solution for detecting changes in the behaviour of the elderly person based on the monitoring of activities of daily living (ADL). The elderly person's daily routine is characterized by the following five indexes: 1) percentage of time lying down, 2) percentage of time sitting, 3) percentage of time standing, 4) percentage of time absent from home, and 5) number of falls during the day. In our framework, these indexes are computed using characteristics extracted from depth and thermal data. We hypothesize that elderly persons have a well-defined, regular life routine, organized around their environment, habits, and social relations. Then, given the indexes values, a day is defined as routine or non-routine day. Thus, looking for changes of day type allows to detect changes in a person's routine. The method has been tested on a database of depth and thermal images recorded in a nursing home over an 85 days period. These tests proved the reliability of the proposed method.

Three-dimensional (3D) integrated renal structures (IRS) segmentation targets segmenting the kidneys, renal tumors, arteries, and veins in one inference. Clinicians will benefit from the 3D IRS visual model for accurate preoperative planning and intraoperative guidance of laparoscopic partial nephrectomy (LPN). However, no success has been reported in 3D IRS segmentation due to the inherent challenges in grayscale distribution: low contrast caused by the narrow task-dependent distribution range of regions of interest (ROIs), and the networks representation preferences caused by the distribution variation inter-images. In this paper, we propose the Meta Greyscale Adaptive Network (MGANet), the first deep learning framework to simultaneously segment the kidney, renal tumors, arteries and veins on CTA images in one inference. It makes innovations in two collaborate aspects: 1) The Grayscale Interest Search (GIS) adaptively focuses segmentation networks on task-dependent grayscale distributions via scaling the window width and center with two cross-correlated coefficients for the first time, thus learning the fine-grained representation for fine segmentation. 2) The Meta Grayscale Adaptive (MGA) learning makes an image-level meta-learning strategy. It represents diverse robust features from multiple distributions, perceives the distribution characteristic, and generates the model parameters to fuse features dynamically according to image's distribution, thus adapting the grayscale distribution variation. This study enrolls 123 patients and the average Dice coefficients of the renal structures are up to 87.9%. Fine selection of the task-dependent grayscale distribution ranges and personalized fusion of multiple representations on different distributions will lead to better 3D IRS segmentation quality. Extensive experiments with promising results on renal structures reveal powerful segmentation accuracy and great clinical significance in renal cancer treatment., Competing Interests: Declaration of Competing Interest Authors declare that they have no conflict of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The resection of small, low-dense or deep lung nodules during video-assisted thoracoscopic surgery (VATS) is surgically challenging. Nodule localization methods in clinical practice typically rely on the preoperative placement of markers, which may lead to clinical complications. We propose a markerless lung nodule localization framework for VATS based on a hybrid method combining intraoperative cone-beam CT (CBCT) imaging, free-form deformation image registration, and a poroelastic lung model with allowance for air evacuation. The difficult problem of estimating intraoperative lung deformations is decomposed into two more tractable sub-problems: (i) estimating the deformation due the change of patient pose from preoperative CT (supine) to intraoperative CBCT (lateral decubitus); and (ii) estimating the pneumothorax deformation, i.e. a collapse of the lung within the thoracic cage. We were able to demonstrate the feasibility of our localization framework with a retrospective validation study on 5 VATS clinical cases. Average initial errors in the range of 22 to 38 mm were reduced to the range of 4 to 14 mm, corresponding to an error correction in the range of 63 to 85%. To our knowledge, this is the first markerless lung deformation compensation method dedicated to VATS and validated on actual clinical data., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Accurate segmentation of uterus, uterine fibroids, and spine from MR images is crucial for high intensity focused ultrasound (HIFU) therapy but remains still difficult to achieve because of 1) the large shape and size variations among individuals, 2) the low contrast between adjacent organs and tissues, and 3) the unknown number of uterine fibroids. To tackle this problem, in this paper, we propose a large kernel Encoder-Decoder Network based on a 2D segmentation model. The use of this large kernel can capture multi-scale contexts by enlarging the valid receptive field. In addition, a deep multiple atrous convolution block is also employed to enlarge the receptive field and extract denser feature maps. Our approach is compared to both conventional and other deep learning methods and the experimental results conducted on a large dataset show its effectiveness.

Fine renal artery segmentation on abdominal CT angiography (CTA) image is one of the most important tasks for kidney disease diagnosis and pre-operative planning. It will help clinicians locate each interlobar artery's blood-feeding region via providing the complete 3D renal artery tree masks. However, it is still a task of great challenges due to the large intra-scale changes, large inter-anatomy variation, thin structures, small volume ratio and small labeled dataset of the fine renal artery. In this paper, we propose the first semi-supervised 3D fine renal artery segmentation framework, DPA-DenseBiasNet, which combines deep prior anatomy (DPA), dense biased network (DenseBiasNet) and hard region adaptation loss (HRA): 1) Based on our proposed dense biased connection, the DenseBiasNet fuses multi-receptive field and multi-resolution feature maps for large intra-scale changes. This dense biased connection also obtains a dense information flow and dense gradient flow so that the training is accelerated and the accuracy is enhanced. 2) DPA features extracted from an autoencoder (AE) are embedded in DenseBiasNet to cope with the challenge of large inter-anatomy variation and thin structures. The AE is pre-trained (unsupervised) by numerous unlabeled data to achieve the representation ability of anatomy features and these features are embedded in DenseBiasNet. This process will not introduce incorrect labels as optimization targets and thus contributes to a stable semi-supervised training strategy that is suitable for sensitive thin structures. 3) The HRA selects the loss value calculation region dynamically according to the segmentation quality so the network will pay attention to the hard regions in the training process and keep the class balanced. Experiments demonstrated that DPA-DenseBiasNet had high predictive accuracy and generalization with the Dice coefficient of 0.884 which increased by 0.083 compared with 3D U-Net (Çiçek et al., 2016). This revealed our framework with great potential for the 3D fine renal artery segmentation in clinical practice., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Image registration represents one of the fundamental techniques in medical imaging and image-guided interventions. In this paper, we present a Convolutional Neural Network (CNN) framework for deformable transesophageal US/CT image registration, for the cardiac arrhythmias, and guidance therapy purposes. The framework consists of a CNN, a spatial transformer, and a resampler. The CNN expects concatenated pairs of moving and fixed images as its input, and estimates as output the parameters for the spatial transformer, which generates the displacement vector field that allows the resampler to wrap the moving image into the fixed image. In our method, we train the model to maximize standard image matching objective functions that are based on the image intensities. The network can be applied to perform non-rigid registration of a pair of CT/US images directly in one pass, avoiding so the time consuming computation of the classical iterative method.

Background: Renal cancer is one of the 10 most common cancers in human beings. The laparoscopic partial nephrectomy (LPN) is an effective way to treat renal cancer. Localization and delineation of the renal tumor from pre-operative CT Angiography (CTA) is an important step for LPN surgery planning. Recently, with the development of the technique of deep learning, deep neural networks can be trained to provide accurate pixel-wise renal tumor segmentation in CTA images. However, constructing the training dataset with a large amount of pixel-wise annotations is a time-consuming task for the radiologists. Therefore, weakly-supervised approaches attract more interest in research., Methods: In this paper, we proposed a novel weakly-supervised convolutional neural network (CNN) for renal tumor segmentation. A three-stage framework was introduced to train the CNN with the weak annotations of renal tumors, i.e. the bounding boxes of renal tumors. The framework includes pseudo masks generation, group and weighted training phases. Clinical abdominal CT angiographic images of 200 patients were applied to perform the evaluation., Results: Extensive experimental results show that the proposed method achieves a higher dice coefficient (DSC) of 0.826 than the other two existing weakly-supervised deep neural networks. Furthermore, the segmentation performance is close to the fully supervised deep CNN., Conclusions: The proposed strategy improves not only the efficiency of network training but also the precision of the segmentation.

Purpose: Surgical treatments for low-rectal cancer require careful considerations due to the low location of cancer in rectums. Successful surgical outcomes highly depend on surgeons' ability to determine clear distal margins of rectal tumors. This is a challenge for surgeons in robot-assisted laparoscopic surgery, since tumors are often concealed in rectums and robotic surgical instruments do not provide tactile feedback for tissue diagnosis in real time. This paper presents the development and evaluation of an intraoperative ultrasound-based augmented reality framework for surgical guidance in robot-assisted rectal surgery., Methods: Framework implementation consists in calibrating the transrectal ultrasound (TRUS) and the endoscopic camera (hand-eye calibration), generating a virtual model and registering it to the endoscopic image via optical tracking, and displaying the augmented view on a head-mounted display. An experimental validation setup is designed to evaluate the framework., Results: The evaluation process yields a mean error of 0.9 mm for the TRUS calibration, a maximum error of 0.51 mm for the hand-eye calibration of endoscopic cameras, and a maximum RMS error of 0.8 mm for the whole framework. In the experiment with a rectum phantom, our framework guides the surgeon to accurately localize the simulated tumor and the distal resection margin., Conclusions: This framework is developed with our clinical partner, based on actual clinical conditions. The experimental protocol and the high level of accuracy show the feasibility of seamlessly integrating this framework within the surgical workflow.

Background and Objective: The prostate cancer interventions, which need an accurate prostate segmentation, are performed under ultrasound imaging guidance. However, prostate ultrasound segmentation is facing two challenges. The first is the low signal-to-noise ratio and inhomogeneity of the ultrasound image. The second is the non-standardized shape and size of the prostate., Methods: For prostate ultrasound image segmentation, this paper proposed an accurate and efficient method of Active shape model (ASM) with Rayleigh mixture model Clustering (ASM-RMMC). Firstly, Rayleigh mixture model (RMM) is adopted for clustering the image regions which present similar speckle distributions. These content-based clustered images are then used to initialize and guide the deformation of an ASM model., Results: The performance of the proposed method is assessed on 30 prostate ultrasound images using four metrics as Mean Average Distance (MAD), Dice Similarity Coefficient (DSC), False Positive Error (FPE) and False Negative Error (FNE). The proposed ASM-RMMC reaches high segmentation accuracy with 95% ± 0.81% for DSC, 1.86 ± 0.02 pixels for MAD, 2.10% ± 0.36% for FPE and 2.78% ± 0.71% for FNE, respectively. Moreover, the average segmentation time is less than 8 s when treating a single prostate ultrasound image through ASM-RMMC., Conclusions: This paper presents a method for prostate ultrasound image segmentation, which achieves high accuracy with less computational complexity and meets the clinical requirements., Competing Interests: Declaration of Competing Interest None., (Copyright © 2019. Published by Elsevier B.V.)

The work aims to develop a new image-processing method to improve the guidance of transesophageal high intensity focused ultrasound (HIFU) atrial fibrillation therapy. Our proposal is a novel registration approach that aligns intraoperative 2D ultrasound with preoperative 3D-CT information. This approach takes advantage of the anatomical constraints imposed at the transesophageal HIFU probe to simplify the registration process. Our proposed method has been evaluated on a physical phantom and on real clinical data.

Tracking the pose of an ultrasound (US) probe is essential for an intraoperative US-based navigation system. The tracking requires mounting a marker on the US probe and calibrating the probe. The goal of the US probe calibration is to determine the rigid transformation between the coordinate system (CS) of the image and the CS of the marker mounted on the probe. We present a fast and automatic calibration method based on a 3D printed phantom and an untracked marker for three-dimensional (3D) US probe calibration. To simplify the conventional calibration procedures using and tracking at least two markers, we used only one marker and did not track it in the whole calibration process. Our automatic calibration method is fast, simple and does not require any experience from the user. The performance of our calibration method was evaluated by point reconstruction tests. The root mean square (RMS) of the point reconstruction errors was 1.39 mm.

Knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance, fibrosis quantification and biophysical modelling. Segmentation of the LA from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is a complex problem. This manuscript presents a benchmark to evaluate algorithms that address LA segmentation. The datasets, ground truth and evaluation code have been made publicly available through the http://www.cardiacatlas.org website. This manuscript also reports the results of the Left Atrial Segmentation Challenge (LASC) carried out at the STACOM'13 workshop, in conjunction with MICCAI'13. Thirty CT and 30 MRI datasets were provided to participants for segmentation. Each participant segmented the LA including a short part of the LA appendage trunk and proximal sections of the pulmonary veins (PVs). We present results for nine algorithms for CT and eight algorithms for MRI. Results showed that methodologies combining statistical models with region growing approaches were the most appropriate to handle the proposed task. The ground truth and automatic segmentations were standardised to reduce the influence of inconsistently defined regions (e.g., mitral plane, PVs end points, LA appendage). This standardisation framework, which is a contribution of this work, can be used to label and further analyse anatomical regions of the LA. By performing the standardisation directly on the left atrial surface, we can process multiple input data, including meshes exported from different electroanatomical mapping systems.

Purpose: In digital x-ray radiography, an antiscatter grid is inserted between the patient and the image receptor to reduce scattered radiation. If the antiscatter grid is used in a stationary way, gridline artifacts will appear in the final image. In most of the gridline removal image processing methods, the useful information with spatial frequencies close to that of the gridline is usually lost or degraded. In this study, a new stationary gridline suppression method is designed to preserve more of the useful information., Methods: The method is as follows. The input image is first recursively decomposed into several smaller subimages using a multiscale 2D discrete wavelet transform. The decomposition process stops when the gridline signal is found to be greater than a threshold in one or several of these subimages using a gridline detection module. An automatic Gaussian band-stop filter is then applied to the detected subimages to remove the gridline signal. Finally, the restored image is achieved using the corresponding 2D inverse discrete wavelet transform., Results: The processed images show that the proposed method can remove the gridline signal efficiently while maintaining the image details. The spectra of a 1D Fourier transform of the processed images demonstrate that, compared with some existing gridline removal methods, the proposed method has better information preservation after the removal of the gridline artifacts. Additionally, the performance speed is relatively high., Conclusions: The experimental results demonstrate the efficiency of the proposed method. Compared with some existing gridline removal methods, the proposed method can preserve more information within an acceptable execution time.

In ultrasound images, tissues are characterized by their speckle texture. Moment-based techniques have proven their ability to capture texture features. However, in ultrasound images, the speckle size increases with the distance from the probe and in some cases the speckle has a concentric texture arrangement. We propose to use moment invariants with respect to image scale and rotation to capture the texture in such cases. Results on synthetic data show that moment invariants are able to characterize the texture but also that some moment orders are sensitive to regions and, moreover, some are sensitive to the boundaries between two different textures. This behavior seems to be very interesting to be used within some segmentation scheme dealing with a combination of regional and boundary information. In this paper we will try to prove the usability of this complementary information in a min-cut/max-flow graph cut scheme., (Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.)

This paper deals with the adaptation, the tuning and the evaluation of the multiple organs Optimal Surface Detection (OSD) algorithm for the T2 MRI prostate segmentation. This algorithm is initialized by first surface approximations of the prostate (obtained after a model adjustment), the bladder (obtained automatically) and the rectum (interactive geometrical model). These three organs are then segmented together in a multiple organs OSD scheme which proposes a competition between the gray level characteristics and some topological and anatomical information of these three organs. This method has been evaluated on the MICCAI Grand Challenge: Prostate MR Image Segmentation (PROMISE) 2012 training dataset.

Image denoising and signal enhancement are the most challenging issues in low dose computed tomography (CT) imaging. Sparse representational methods have shown initial promise for these applications. In this work we present a wavelet based sparse representation denoising technique utilizing dictionary learning and clustering. By using wavelets we extract the most suitable features in the images to obtain accurate dictionary atoms for the denoising algorithm. To achieve improved results we also lower the number of clusters which reduces computational complexity. In addition, a single image noise level estimation is developed to update the cluster centers in higher PSNRs. Our results along with the computational efficiency of the proposed algorithm clearly demonstrates the improvement of the proposed algorithm over other clustering based sparse representation (CSR) and K-SVD methods.

This paper deals about high intensity ultrasound interstitial therapy simulation. The simulated phased array ultrasound probe allows a dynamic electronic focusing of the therapeutic beam. In order to maximize the power deposit at the focal point we propose a method which allows to optimally defining the phase shift of the electrical control signal for each individual element.

Prostate segmentation in MRI may be difficult at the interface with the bladder where the contrast is poor. Coupled-models that segment simultaneously both organs with non-overlapping constraints offer a good solution. As a pre-segmentation of the structures of interest is required, we propose in this paper a fast deformable model to segment the bladder. The combination of inflation and internal forces, locally adapted according to the gray levels, allow to deform the mesh toward the boundaries while overcoming the leakage issues that can occur at weak edges. The algorithm, evaluated on 33 MRI volumes from 5 different devices, has shown good performance providing a smooth and accurate surface.

This survey on medical imaging provides a look into three major components. The first one deals with the full steps through which it must be apprehended: from the sensors to the reconstruction, from the image analysis up to its interpretation. The second aspect describes the physical principles used for imaging (magnetic resonance, acoustic, optics, etc.). The last section shows how imaging is involved in therapeutic procedures and in particular the new physical therapies. All along this paper, the research perspectives are sketched.

This paper describes a modeling method of the tissue temperature evolution over time in hyper or hypothermia. The tissue temperature evolution over time is classically described by Pennes' bioheat transfer equation which is generally solved by a finite difference method. In this paper we will present a method where the bioheat transfer equation can be algebraically solved after a Fourier transformation over the space coordinates. As an example, we implemented this method for the simulation of a percutaneous high intensity ultrasound hepatocellular carcinoma curative treatment and compared it with the finite difference method and experimental data., (2009 Elsevier Ltd. All rights reserved.)

This paper describes a fast and fully automatic method for liver vessel segmentation on computerized tomography scan preoperative images. The basis of this method is the introduction of a 3-D geometrical moment-based detector of cylindrical shapes within the minimum-cut/maximum-flow energy minimization framework. This method represents an original way to introduce a data term as a constraint into the widely used Boykov's graph cuts algorithm, and hence, to automate the segmentation. The method is evaluated and compared with others on a synthetic dataset. Finally, the relevancy of our method regarding the planning of a necessarily accurate percutaneous high-intensity focused ultrasound surgical operation is demonstrated with some examples.

Beyond the advances made in computer-assisted interventions and robotic systems, the demand for more efficient and safer therapies remains challenging. Thus, if it is possible to improve the instrument tracking, steering, and target localization, to miniaturize the sensors and actuators, and to conduct preoperatively planned minimally invasive therapies, we still need new resources to achieve permanent destruction of abnormal tissues or suppression of pathological processes. Most of the physics-based (or energy-based) therapeutic principles at our disposal have been established a long time ago, but their actions on basic cellular and molecular mechanisms are not yet fully understood. They all have a wide spectrum of clinical targets in terms of organs and pathologies, modes of application (external, interstitial, intraluminal, etc.) with advantages and side-effect drawbacks, proven indications, and contraindications. Some of them may still face controversies regarding their outcomes. This short article, mainly focused on tumor destruction, briefly reviews in its first part some of these techniques and sketches the next generation under investigation. The former include radio frequency (RF), high-intensity focused ultrasound (HiFU), microwaves, and cryotherapy, of which all are temperature based. Laser-based approaches [e.g., photodynamic therapy (PDT) at large] are also discussed. Radiotherapy and its variants (hadrontherapy, brachytherapy, Gamma Knife, and CyberKnife) remain, of course, as the reference technique in cancer treatment. The next breakthroughs are examined in the second part of the article. They are based on the close association between imaging agents, drugs, and some stimulation techniques. The ongoing research efforts in that direction show that, if they are still far from clinical applications, strong expectations are made. From the point of view of interventional planning and image guidance, all of them share a lot of concerns.

Learning embryology remains difficult, since it requires understanding of many complex phenomena. The temporal evolution of developmental events has classically been illustrated using cartoons, which create difficulty in linking spatial and temporal aspects, such correlation being the keystone of descriptive embryology. We synthesized the bibliographic data from recent studies of atrial septal development. On the basis of this synthesis, consensus on the stages of atrial septation as seen in the human heart has been reached by a group of experts in cardiac embryology and pediatric cardiology. This has permitted the preparation of three-dimensional (3D) computer graphic objects for the anatomical components involved in the different stages of normal human atrial septation. We have provided a virtual guide to the process of normal atrial septation, the animation providing an appreciation of the temporal and morphologic events necessary to separate the systemic and pulmonary venous returns. We have shown that our animations of normal human atrial septation increase significantly the teaching of the complex developmental processes involved, and provide a new dynamic for the process of learning.

The goal of our work is to propose a fast ultrasound image simulation from CT volumes. This method is based on a model elaborated by Bamber and Dickinson that predict the appearance and properties of a B-Scan ultrasound image from the distribution of point scatterers. We propose to extend this model for the standard medical ultrasound image simulation by taking into account the acoustical tissue properties (scatterer distribution) and the geometry and the specifications of the ultrasound probe (circular probe, number and size of transducers, US pulse frequency and bandwidth, etc.). Simulations have been obtained in a fairly fast computation speed and qualitatively they present most of the real ultrasound image characteristics.

This letter describes a temperature-varying attenuation approach for preoperative planning of high intensity ultrasound interstitial targeted therapy. Such approach is mainly aimed at the treatment of primary liver cancer for which a precise lesion control must be achieved. It is shown through simulation that the shape and size of the resulting necrotic volume is significantly different from the one obtained when this tissue property is considered constant in time.

This paper describes a modeling method of the tissue temperature evolution over time in hyperthermia. More precisely, this approach is used to simulate the hepatocellular carcinoma curative treatment by a percutaneous high intensity ultrasound surgery. The tissue temperature evolution over time is classically described by Pennes' bioheat transfer equation which is generally solved by a finite difference method. In this paper we will present a method where the bioheat transfer equation can be algebraically solved after a Fourier transformation over the space coordinates. The implementation and boundary conditions of this method will be shown and compared with the finite difference method.

The CT uroscan contains three to four time-spaced acquisitions of the same patient. Registration of these acquisitions forms a vectorial volume, which contains a more complete anatomical information. In order to outline the anatomical structures, multi-dimensional classification is necessary for analyzing this vectorial volume. Because of the partial volume effect (PVE), probability distributions are assigned to the different material types within this vectorial volume instead of a definite material distribution. Gaussian mixture model is often used in probability classification problems to model such distributions, but it relies only on the intensity distributions, which will lead a misclassification on the boundaries and inhomogeneous regions with noises. In order to solve this problem, a neighborhood weighted Gaussian mixture model is proposed in this paper. Expectation Maximization algorithm is used as optimization method. The experiments demonstrate that the proposed method can get a better classification result and less affected by the noise.

One of the goal of the Nephron-Sparing Surgery properative planning is to delineate as exactly as possible the renal carcinoma and to specify its relations to the renal arterial, venous and collecting system anatomies. The classical preoperative imaging system is the Spiral CT Urography, which gives sucessive 3D acquisitions of complementary information The integration of this information within the a patient spacific anatomical referential can be achieved by intra-patient registration techniques. A local MI maximization registration method is proposed in this paper. The kidneys are extracted from the abdomen volumes and then the registration between the extracted kidneys is implemented by maximizing the MI between them. The experimental results demonstrates that this method is effective.

Within a specific medical application, the primary liver cancer curative treatment by a percutaneous high intensity ultrasound surgery, our study was designed to propose a fast 3D semi-automatic segmentation method of the liver, the tumor and the hepatic vascular networks. This method is characterized by a graph description of contrast medium injected CT volume where the links between the nodes describe either the degrees of similarity between voxels of the same class (region based approach) or the class changes between two neighboring voxels (boundary based approaches). The various weights describing these two properties are defined after a first interactive training phase. A Max-Flow/Min-Cut graph cut algorithm allowed partitioning the volume in two representative subsets of the segmented classes.

This paper presents a three-dimensional (3-D) shape reconstruction/intrapatient rigid registration technique used to establish a Nephron-Sparing Surgery preoperative planning. The usual preoperative imaging system is the Spiral CT Urography, which provides successive 3-D acquisitions of complementary information on kidney anatomy. Because the kidney is difficult to demarcate from the liver or from the spleen only limited information on its volume or surface is available. In this paper, we propose a methodology allowing a global kidney spatial representation on a spherical harmonics basis. The spherical harmonics are exploited to recover the kidney 3-D shape and also to perform intrapatient 3-D rigid registration. An evaluation performed on synthetic data showed that this technique presented lower performance then expected for the 3-D shape recovering but exhibited registration results slightly more accurate as the iterative closest point technique with faster computation time.

Improvements in the diagnosis of congenital malformations explain the increasing early termination of pregnancies. Before 13 weeks of gestation, an accurate in vivo anatomic diagnosis cannot currently be made in all fetuses with current imaging instrumentation. Anatomopathologic examinations remain the gold standard to make accurate diagnoses, although they reach limits between 9 and 13 weeks of gestation. We present the first results of a methodology that can be applied routinely, using standard histologic section, thus enabling the reconstruction, visual estimate, and quantitative analysis of 13-week human embryonic cardiac structures. The cardiac blocks were fixed, embedded in paraffin, and entirely sliced by a microtome. One of 10 slices was topographically colored and digitized on an optical microscope. Cardiac volume was recovered by semiautomatic realignment of the sections. Another semiautomatic procedure allowed extracting and labeling of cardiac structures from the volume. Structures were studied with display tools, which disclosed the internal and external cardiac components and enabled determination of size, thickness, and precise positioning of ventricles, atria, and large vessels. This pilot study confirmed that a new 3-dimensional reconstruction and visualization method enables accurate diagnoses, including in embryos younger than 13 weeks. Its implementation at earlier stages of embryogenesis will provide a clearer view of cardiac development.

One of the goal of the Nephron-Sparing Surgery preoperative planning is to delineate as exactly as possible the renal carcinoma and to specify its relations to the renal arterial, venous and collecting system anatomy. The classical preoperative imaging system is the spiral CT urography, which gives successive 3D acquisitions of complementary information about the kidney anatomy. The fusion of this information can be achieved by intra-patient registration techniques. However, because the kidney is difficult to demarcate from the liver or the spleen, a partial information registration technique should be used. In our paper we propose a methodology based on spherical harmonics, which can be used for partial information registration and also for the 3D kidney form modeling.

An improved and very fast algorithm dealing with the extraction of vessels in three-dimensional imaging is described. The approach is based on geometrical moments and a local cylindrical approximation. A robust estimation of vessel and background intensity levels, position, orientation, and diameter of the vessels with adaptive control of key parameters, is provided during vessel tracking. Experimental results are presented for lower limb arteries in multidetector computed tomography scanner.

This paper is aimed at understanding epileptic patient disorders through the analysis of surface electroencephalograms (EEG). It deals with the detection of spikes or spike-waves based on a nonorthogonal wavelet transform. A multilevel structure is described that locates the temporal segments where abnormal events occur. These events are then visually interpreted by means of a 3D mapping technique. This 3D display makes use of a ray tracing scheme and combines both the functional (the EEG but also its wavelet representation) and the morphological data (acquired from computed tomography [CT] or magnetic resonance imaging [MRI] devices). The results show that a significant reduction of the clinical workload is obtained while the most important episodes are better reviewed and analyzed.

This paper presents a three-dimensional edge operator aimed at the detection of anatomical structures in medical imaging. It uses the spatial moments of gray level surface, and operates in three dimensions with any window size. It allows us to estimate the location and the contrast surface, as well as the surface orientation. The computation of the discrete version is reported. Bias and errors due to the spatial sampling and noise are analyzed both at a theoretical and experimental level. The moment-based operator is compared with other well-known edge operators on simple shaped primitives for which the analytical solution is known. The 3D rendering of real data is then provided by merging the operator in a ray-tracing framework.

This paper describes a new method for the three-dimensional (3-D) tracking and the quantification of blood vessels from magnetic resonance angiography (MRA). The approach is based on 3-D geometrical moments and consists of the following steps: (1) interactive selection of 3-D seed points; (2) automatic tracking of the vessels; (3) local computation of both diameter and orientation; (4) rendering of the vessels. This detection and estimation scheme has been validated on simulated and real data.