Segmentation of cardiac infarction in delayed-enhancement MRI using probability map and transformers-based neural networks.

BACKGROUND AND OBJECTIVE: Automatic segmentation of myocardial infarction is of great clinical interest for the quantitative evaluation of myocardial infarction (MI). Late Gadolinium Enhancement cardiac MRI (LGE-MRI) is commonly used in clinical practice to quantify MI, which is crucial for clinical diagnosis and treatment of cardiac diseases. However, the segmentation of infarcted tissue in LGE-MRI is highly challenging due to its high anisotropy and inhomogeneities. METHODS: The innovative aspect of our work lies in the utilization of a probability map of the healthy myocardium to guide the localization of infarction, as well as the combination of 2D U-Net and U-Net transformers to achieve the final segmentation. Instead of employing a binary segmentation map, we propose using a probability map of the normal myocardium, obtained through a dedicated 2D U-Net. To leverage spatial information, we employ a U-Net transformers network where we incorporate the probability map into the original image as an additional input. Then, To address the limitations of U-Net in segmenting accurately the contours, we introduce an adapted loss function. RESULTS: Our method has been evaluated on the 2020 MICCAI EMIDEC challenge dataset, yielding competitive results. Specifically, we achieved a Dice score of 92.94% for the myocardium and 92.36% for the infarction. These outcomes highlight the competitiveness of our approach. CONCLUSION: In the case of the infarction class, our proposed method outperforms state-of-the-art techniques across all metrics evaluated in the challenge, establishing its superior performance in infarction segmentation. This study further reinforces the importance of integrating a contour loss into the segmentation process.

Evaluation of the Needle Positioning Accuracy of a Light Puncture Robot Under MRI Guidance: Results of a Clinical Trial on Healthy Volunteers.

PURPOSE: To assess the accuracy of Light Puncture Robot (LPR) as a patient-mounted robot, in positioning a sham needle under MRI guidance for abdominal percutaneous interventions. MATERIALS AND METHODS: This monocentric, prospective and non-controlled study was approved by the ethics review board. The study evaluated the accuracy of LPR V3 to achieve a virtual puncture in 20 healthy volunteers. Three trajectories were tried on each volunteer, under 3-T MRI guidance. RESULTS: Accuracy under 5 mm in attaining a 10 cm-deep target was reached in 72% of attempts after 2 robot motions with a median error of 4.1 mm [2.1; 5.1]. Median procedure time for one trajectory was 12.9 min [10.2; 18.0] and median installation time was 9.0 min [6.0; 13.0]. CONCLUSION: LPR accuracy in the deployment of a sham needle inside the MRI tunnel and its setup time are promising. Further studies need to be conducted to confirm these results before clinical trials.

Blockwise processing applied to brain microvascular network study.

The study of cerebral microvascular networks requires high-resolution images. However, to obtain statistically relevant results, a large area of the brain (several square millimeters) must be analyzed. This leads us to consider huge images, too large to be loaded and processed at once in the memory of a standard computer. To consider a large area, a compact representation of the vessels is required. The medial axis is the preferred tool for this application. To extract it, a dedicated skeletonization algorithm is proposed. Numerous approaches already exist which focus on computational efficiency. However, they all implicitly assume that the image can be completely processed in the computer memory, which is not realistic with the large images considered here. We present in this paper a skeletonization algorithm that processes data locally (in subimages) while preserving global properties (i.e., homotopy). We then show some results obtained on a mosaic of three-dimensional images acquired by confocal microscopy.

Segmentation, Separation and Pose Estimation of Prostate Brachytherapy Seeds in CT Images.

GOAL: In this paper, we address the development of an automatic approach for the computation of pose information (position + orientation) of prostate brachytherapy loose seeds from 3-D CT images. METHODS: From an initial detection of a set of seed candidates in CT images using a threshold and connected component method, the orientation of each individual seed is estimated by using the principal components analysis method. The main originality of this approach is the ability to classify the detected objects based on a priori intensity and volume information and to separate groups of closely spaced seeds using three competing clustering methods: the standard and a modified k-means method and a Gaussian mixture model with an expectation-maximization algorithm. Experiments were carried out on a series of CT images of two phantoms and patients. The fourteen patients correspond to a total of 1063 implanted seeds. Detections are compared to manual segmentation and to related work in terms of detection performance and calculation time. RESULTS: This automatic method has proved to be accurate and fast including the ability to separate groups of seeds in a reliable way and to determine the orientation of each seed. SIGNIFICANCE: Such a method is mandatory to be able to compute precisely the real dose delivered to the patient postoperatively instead of assuming the alignment of seeds along the theoretical insertion direction of the brachytherapy needles.

Using CamiTK for rapid prototyping of interactive computer assisted medical intervention applications.

Computer Assisted Medical Intervention (CAMI hereafter) is a complex multi-disciplinary field. CAMI research requires the collaboration of experts in several fields as diverse as medicine, computer science, mathematics, instrumentation, signal processing, mechanics, modeling, automatics, optics, etc. CamiTK is a modular framework that helps researchers and clinicians to collaborate together in order to prototype CAMI applications by regrouping the knowledge and expertise from each discipline. It is an open-source, cross-platform generic and modular tool written in C++ which can handle medical images, surgical navigation, biomedicals simulations and robot control. This paper presents the Computer Assisted Medical Intervention ToolKit (CamiTK) and how it is used in various applications in our research team.

Interventional radiology robot for CT and MRI guided percutaneous interventions.

This paper introduces a new patient-mounted CT and MRI guided interventional radiology robot for percutaneous needle interventions. The 5 DOF robot uses ultrasonic motors and pneumatics to position the needle and then insert it progressively. The needle position and inclination can be registered in the images using two strategically placed fiducials visible in both imaging modalities. A first prototype is presented and described in terms of its sterilization, CT and MRI compatibility, and precision. Tests showed that 1) it is entirely sterilizable with hydrogen peroxide gas, 2) no image artifacts or deformations are noticeable in the CT and MRI images, 3) does not affect the SNR of MR images, and 4) its mechanical error is less than 5mm.

A novel three-dimensional computer-assisted method for a quantitative study of microvascular networks of the human cerebral cortex.

OBJECTIVE: Detailed information on microvascular network anatomy is a requirement for understanding several aspects of microcirculation, including oxygen transport, distributions of pressure, and wall shear stress in microvessels, regulation of blood flow, and interpretation of hemodynamically based functional imaging methods, but very few quantitative data on the human brain microcirculation are available. The main objective of this study is to propose a new method to analyze this microcirculation. METHODS: From thick sections of india ink-injected human brain, using confocal laser microscopy, the authors developed algorithms adapted to very large data sets to automatically extract and analyze center lines together with diameters of thousands of brain microvessels within a large cortex area. RESULTS: Direct comparison between the original data and the processed vascular skeletons demonstrated the high reliability of this method and its capability to manage a large amount of data, from which morphometry and topology of the cerebral microcirculation could be derived. CONCLUSIONS: Among the many parameters that can be analyzed by this method, the capillary size, the frequency distributions of diameters and lengths, the fractal nature of these networks, and the depth-related density of vessels are all vital features for an adequate model of cerebral microcirculation.