Embedding expertise knowledge into inverse treatment planning for low-dose-rate brachytherapy of hepatic malignancies.

BACKGROUND: Leveraging the precision of its radiation dose distribution and the minimization of postoperative complications, low-dose-rate (LDR) permanent seed brachytherapy is progressively adopted in addressing hepatic malignancies. PURPOSE: The present study endeavors to devise a sophisticated treatment planning system (TPS) to optimize LDR brachytherapy for hepatic lesions. METHODS: Our TPS encompasses four integral modules: multi-organ segmentation, seed distribution initialization, puncture pathway selection, and inverse dose planning. By amalgamating an array of deep learning models, the segmentation module proficiently labels 17 discrete abdominal targets within the images. We introduce a knowledge-based seed distribution initialization methodology that discerns the most analogous tumor shape in the reference treatment plan from the knowledge base. Subsequently, the seed distribution from the reference plan is transmuted to the current case, thus establishing seed distribution initialization. Furthermore, we parameterize the puncture needles and seeds, while concurrently constraining the puncture needle angle through the employment of a virtual puncture panel to augment planning algorithm efficiency. We also presented a user interface that includes a range of interactive features, seamlessly integrated with the treatment planning generation function. RESULTS: The multi-organ segmentation module, which is trained by 50 cases of in-house CT scans and 694 cases of publicly available CT scans, achieved average Dice of 0.80 and Hausdorff distance of 5.2 mm in testing datasets. The results demonstrate that knowledge-based initialization exhibits a marked enhancement in expediting the convergence rate. Our TPS also demonstrates a dominant advantage in dose-volume-histogram criteria and execution time in comparison to commercial TPS. CONCLUSION: The study proposes an innovative treatment planning system for low-dose-rate permanent seed brachytherapy for hepatic malignancies. We show that the generated treatment plans meet clinical requirement.

3D/2D Vessel Registration Based on Monte Carlo Tree Search and Manifold Regularization.

The augmented intra-operative real-time imaging in vascular interventional surgery, which is generally performed by projecting preoperative computed tomography angiography images onto intraoperative digital subtraction angiography (DSA) images, can compensate for the deficiencies of DSA-based navigation, such as lack of depth information and excessive use of toxic contrast agents. 3D/2D vessel registration is the critical step in image augmentation. A 3D/2D registration method based on vessel graph matching is proposed in this study. For rigid registration, the matching of vessel graphs can be decomposed into continuous states, thus 3D/2D vascular registration is formulated as a search tree problem. The Monte Carlo tree search method is applied to find the optimal vessel matching associated with the highest rigid registration score. For nonrigid registration, we propose a novel vessel deformation model based on manifold regularization. This model incorporates the smoothness constraint of vessel topology into the objective function. Furthermore, we derive simplified gradient formulas that enable fast registration. The proposed technique undergoes evaluation against seven rigid and three nonrigid methods using a variety of data - simulated, algorithmically generated, and manually annotated - across three vascular anatomies: the hepatic artery, coronary artery, and aorta. Our findings show the proposed method's resistance to pose variations, noise, and deformations, outperforming existing methods in terms of registration accuracy and computational efficiency. The proposed method demonstrates average registration errors of 2.14 mm and 0.34 mm for rigid and nonrigid registration, and an average computation time of 0.51 s.

PixelTopoIS: a pixel-topology-coupled guidewire tip segmentation framework for robot-assisted intervention.

PURPOSE: Existing works showed great performance in pixel-level guidewire segmentation. However, topology-level segmentation has not been fully exploited in these works. Guidewire (tip) endpoint localization and (guidewire) loop detection are typical topology-level guidewire segmentation tasks. A superb guidewire segmentation algorithm should achieve both low endpoint localization error and high loop detection accuracy. METHODS: This paper focuses on pixel-topology-coupled guidewire (tip) segmentation. The contributions are (1) two algorithmic improvements including an iterative segmentation framework and a pixel-topology-coupled loss function (2) a new metric that comprehensively evaluates the segmentation results at both pixel and topology level (3) the first publicly available guidewire dataset (The dataset can be downloaded from www.njzdyyrobocgsu.com ) containing 4500+ X-ray images with radiologist-annotated results. RESULTS: The algorithm rivals the state-of-the-art methods in pixel-level metric (0.06-4.21% for the F1-score) in most sequences, achieving performance comparable to the best method on two sequences. Our method also shows competitive performance (20% for the loop existence accuracy) on the newly introduced metric. Experiments are also performed to quantitatively validate the functionality of different components in our framework. CONCLUSION: The framework is effective in segmenting the guidewire by considering pixel and topology equally, providing an accurate position of the tip's endpoint (pixel-level) to the surgeon/robot and preserving the clinically meaningful guidewire structure (topology-level) simultaneously.