3D motion tracking in X-ray image-guided operations using pre- and intra-operative image registration has recently gained attention. However, due to pre- and intra-operative acquisitions exist spatio-temporal misalignment (i.e., limited 3D prior versus continuous 2D images) and distinct respiratory phase difference, recent methods still struggle to accurately estimate 3D dynamic structures from X-ray images. To overcome these issues, we propose a novel double-decomposition tracking (DD-Track) framework that aligns with multi-organ motion characteristics via two alignment pipes: 1) Temporal alignment aims to compensate in-plane respiratory phases difference between the projection of static 3D prior and continuous X-ray images. A dual-excitation mechanism in the image and frequency domains is proposed to extract discriminate motion features while suppressing irrelevant background information. 2) Spatial alignment subsequently integrates the extracted 2D motion features into the cross-modal registration process to accurately warp the 3D prior. Further, we decompose the motion tracking into the common trajectory and organ-specific deformation to align with the multi-organ motion nature, avoiding excessive organ stretching for sliding compensation. Comprehensive quantitative and qualitative experiments on simulated and clinical multi-organ datasets demonstrate that DD-Track outperforms state-of-the-art methods, and we also validate its generalization for tracking intra-organ lesions on simulated data.

Dynamic visualization of the hepatic vessel is crucial in X-ray image-guided transjugular intrahepatic portosystemic shunt (TIPS) procedures. However, intraoperative breathing and the presence of guidewires complicate the prediction of the vessel position and posture without contrast agents. The respiration compensation technique aims to utilize the intraoperative respiration modeling to deform the initial vessel roadmap, thereby achieving the dynamic vessel prediction in the X-ray image sequence for the interventional guidance. Therefore, we propose a novel respiration compensation framework utilizing the adaptive tracking and bending energy modeling to achieve the stable vessel roadmap prediction under free breathing. First, we introduce the inter-frame rigid displacement compensation module based on the domain adaptation and adaptive centroid tracking. This module fits the respiratory curve from the X-ray images, providing the temporal motion priors for aligning roadmaps across frames. Second, we propose the novel deformation compensation module based on the bending energy modeling to correct the respiratory motion, wherein we utilize the energy features of the guidewires to drive the non-rigid registration. The control points sampled by the bending energy guide the local image to form the deformation field, facilitating the dynamic overlap of the vessel roadmaps in X-ray images. Experimental results on simulated and clinical datasets show an average tracking error of 0.95 $\pm$ 0.26 mm and 1.49 $\pm$ 0.40 mm, respectively. The effective and fast (mean 57 ms per frame) compensation achieved by our framework has the potential for improving the outcome of liver intervention and reducing the reliance on contrast agents.

Accurately reconstructing 4D critical organs contributes to the visual guidance in X-ray image-guided interventional operation. Current methods estimate intraoperative dynamic meshes by refining a static initial organ mesh from the semantic information in the single-frame X-ray images. However, these methods fall short of reconstructing an accurate and smooth organ sequence due to the distinct respiratory patterns between the initial mesh and X-ray image. To overcome this limitation, we propose a novel dual-stage complementary 4D organ reconstruction (DSC-Recon) model for recovering dynamic organ meshes by utilizing the preoperative and intraoperative data with different respiratory patterns. DSC-Recon is structured as a dual-stage framework: 1) The first stage focuses on addressing a flexible interpolation network applicable to multiple respiratory patterns, which could generate dynamic shape sequences between any pair of preoperative 3D meshes segmented from CT scans. 2) In the second stage, we present a deformation network to take the generated dynamic shape sequence as the initial prior and explore the discriminate feature (i.e., target organ areas and meaningful motion information) in the intraoperative X-ray images, predicting the deformed mesh by introducing a designed feature mapping pipeline integrated into the initialized shape refinement process. Experiments on simulated and clinical datasets demonstrate the superiority of our method over state-of-the-art methods in both quantitative and qualitative aspects.

Accurate segmentation of liver vessel from CTA image is important for the diagnosis and treatment of liver diseases. The quality of labeled data directly affects the prediction results of the segmentation model. Compared with CTA image, MRA image has clearer 3D vasculature. Therefore, in order to reduce the reliance of the labeled CTA image which may contain ambiguous vessel contours, we propose a novel unsupervised liver vessel segmentation method based on image domain adaptive alignment network (IDAA-Net) by using labeled MRA and unlabeled CTA images. The IDAA-Net mainly contains three modules: 1) A spatial alignment module (SAM) is introduced to convert MRA image slice to synthetic CTA image slice for achieving spatial alignment of the different modality data in the feature and image levels; 2) An artifact removal module (ARM) is designed to eliminate background artifacts of synthetic CTA from SAM by using the liver label in MRA; 3) An adversarial segmentation module (ASM) is proposed to obtain the optimal segmentation by jointly adversarial learning and supervised learning between the predicted segmentation and the ground-truth label of MRA image. Experiments on the public and private datasets show that our method achieves comparable performance with state-of-the-art supervised method and outperforms the existing unsupervised segmentation methods.

Accurate segmentation of liver vessel from CTA images is often challenging due to the limited availability of labeled data. In the field of medical image segmentation, semi-supervised learning has garnered significant attention as it utilizes unlabeled data to enhance the training of segmentation models. In this paper, we propose a novel cross-pseudo semi-supervised network (CPSS-Net) based on nnU-Net. The CPSS-Net contains three innovative components: 1) An probability prediction (PP) module is designed to generate probability maps for both labeled and unlabeled datasets, capturing model uncertainty through parallel nnU-Net; 2) A double pseudo-label (DPL) module is used to convert the predicted probability maps into double soft pseudo-labels using an adaptive sharpening function; 3) A cross pseudo-supervised (CPS) module is introduced to learn the mutual consistency of double pseudo-labels. Test experiments on both public and private datasets show that our method achieved a Dice score of 0.67 and a sensitivity score of 0.69, surpassing the segmentation accuracy of existing related methods.