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1.
BMC Med Inform Decis Mak ; 23(1): 247, 2023 11 03.
Article in English | MEDLINE | ID: mdl-37924054

ABSTRACT

BACKGROUND: Clinical practice guidelines (CPGs) are designed to assist doctors in clinical decision making. High-quality research articles are important for the development of good CPGs. Commonly used manual screening processes are time-consuming and labor-intensive. Artificial intelligence (AI)-based techniques have been widely used to analyze unstructured data, including texts and images. Currently, there are no effective/efficient AI-based systems for screening literature. Therefore, developing an effective method for automatic literature screening can provide significant advantages. METHODS: Using advanced AI techniques, we propose the Paper title, Abstract, and Journal (PAJO) model, which treats article screening as a classification problem. For training, articles appearing in the current CPGs are treated as positive samples. The others are treated as negative samples. Then, the features of the texts (e.g., titles and abstracts) and journal characteristics are fully utilized by the PAJO model using the pretrained bidirectional-encoder-representations-from-transformers (BERT) model. The resulting text and journal encoders, along with the attention mechanism, are integrated in the PAJO model to complete the task. RESULTS: We collected 89,940 articles from PubMed to construct a dataset related to neck pain. Extensive experiments show that the PAJO model surpasses the state-of-the-art baseline by 1.91% (F1 score) and 2.25% (area under the receiver operating characteristic curve). Its prediction performance was also evaluated with respect to subject-matter experts, proving that PAJO can successfully screen high-quality articles. CONCLUSIONS: The PAJO model provides an effective solution for automatic literature screening. It can screen high-quality articles on neck pain and significantly improve the efficiency of CPG development. The methodology of PAJO can also be easily extended to other diseases for literature screening.


Subject(s)
Deep Learning , Practice Guidelines as Topic , Humans , Artificial Intelligence , Clinical Decision-Making , Neck Pain , Review Literature as Topic
2.
J Vasc Interv Radiol ; 31(12): 2098-2103, 2020 Dec.
Article in English | MEDLINE | ID: mdl-33261744

ABSTRACT

PURPOSE: To investigate an augmented reality (AR)-guided endovascular puncture to facilitate successful transjugular intrahepatic portosystemic shunt (TIPS). MATERIALS AND METHODS: An AR navigation system for TIPS was designed. Three-dimensional (3D) liver models including portal and hepatic vein anatomy were extracted from preoperative CT images. The 3D models, intraoperative subjects, and electromagnetic tracking information of the puncture needles were integrated through the system calibration. In the AR head-mounted display, the 3D models were overlaid on the subjects, which was a liver phantom in the first phase and live beagle dogs in the second phase. One life-size liver phantom and 9 beagle dogs were used in the experiments. Imaging after puncture was performed to validate whether the needle tip accessed the target hepatic vein successfully. RESULTS: Endovascular punctures of the portal vein of the liver phantom were repeated 30 times under the guidance of the AR system, and the puncture needle successfully accessed the target vein during each attempt. In the experiments of live canine subjects, the punctures were successful in 2 attempts in 7 beagle dogs and in 1 attempt in the remaining 2 dogs. The puncture time of needle from hepatic vein to portal vein was 5-10 s in the phantom experiments and 10-30 s in the canine experiments. CONCLUSIONS: The feasibility of AR-based navigation facilitating accurate and successful portal vein access in preclinical models of TIPS was validated.


Subject(s)
Augmented Reality , Endovascular Procedures/instrumentation , Hepatic Veins/surgery , Portal Vein/surgery , Portasystemic Shunt, Transjugular Intrahepatic , Radiography, Interventional , Surgery, Computer-Assisted/instrumentation , Animals , Computed Tomography Angiography , Dogs , Feasibility Studies , Hepatic Veins/diagnostic imaging , Humans , Models, Animal , Phlebography , Portal Vein/diagnostic imaging , Portasystemic Shunt, Transjugular Intrahepatic/instrumentation , Predictive Value of Tests , Punctures , Radiography, Interventional/instrumentation , Smart Glasses
3.
BMC Med Inform Decis Mak ; 19(Suppl 6): 270, 2019 12 19.
Article in English | MEDLINE | ID: mdl-31856807

ABSTRACT

BACKGROUND: Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures. METHODS: A novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method. RESULTS: Experiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence. CONCLUSIONS: The proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.


Subject(s)
Algorithms , Coronary Angiography/methods , Image Processing, Computer-Assisted/methods , Humans , Image Enhancement/methods , Principal Component Analysis , Sensitivity and Specificity
4.
Biomed Eng Online ; 16(1): 16, 2017 Jan 14.
Article in English | MEDLINE | ID: mdl-28088195

ABSTRACT

BACKGROUND: Automated image segmentation has benefits for reducing clinicians' workload, quicker diagnosis, and a standardization of the diagnosis. METHODS: This study proposes an automatic liver segmentation approach based on appearance and context information. The relationship between neighboring pixels in blocks is utilized to estimate appearance information, which is used for training the first classifier and obtaining the probability distribution map. The map is used for extracting context information, along with appearance features, to train the next classifier. The prior probability distribution map is achieved after iterations and refined through an improved random walk for liver segmentation without user interaction. RESULTS: The proposed approach is evaluated using CT images with eight contemporary approaches, and it achieves the highest VOE, RVD, ASD, RMSD and MSD. It also achieves a high average score of 76 using the MICCAI-2007 Grand Challenge scoring system. CONCLUSIONS: Experimental results show that the proposed method is superior to eight other state of the art methods.


Subject(s)
Image Processing, Computer-Assisted/methods , Liver/anatomy & histology , Liver/diagnostic imaging , Humans , Pattern Recognition, Automated , Probability , Tomography, X-Ray Computed
5.
Biomed Eng Online ; 16(1): 124, 2017 Oct 30.
Article in English | MEDLINE | ID: mdl-29084564

ABSTRACT

BACKGROUND: 3D ultrasound volume reconstruction from B-model ultrasound slices can provide more clearly and intuitive structure of tissue and lesion for the clinician. METHODS: This paper proposes a novel Global Path Matching method for the 3D reconstruction of freehand ultrasound images. The proposed method composes of two main steps: bin-filling scheme and hole-filling strategy. For the bin-filling scheme, this study introduces two operators, including the median absolute deviation and the inter-quartile range absolute deviation, to calculate the invariant features of each voxel in the 3D ultrasound volume. And the best contribution range for each voxel is obtained by calculating the Euclidian distance between current voxel and the voxel with the minimum invariant features. Hence, the intensity of the filling vacant voxel can be obtained by weighted combination of the intensity distribution of pixels in the best contribution range. For the hole-filling strategy, three conditions, including the confidence term, the data term and the gradient term, are designed to calculate the weighting coefficient of the matching patch of the vacant voxel. While the matching patch is obtained by finding patches with the best similarity measure that defined by the three conditions in the whole 3D volume data. RESULTS: Compared with VNN, PNN, DW, FMM, BI and KR methods, the proposed Global Path Matching method can restore the 3D ultrasound volume with minimum difference. CONCLUSIONS: Experimental results on phantom and clinical data sets demonstrate the effectiveness and robustness of the proposed method for the reconstruction of ultrasound volume.


Subject(s)
Algorithms , Imaging, Three-Dimensional/methods , Ultrasonography
6.
J Xray Sci Technol ; 24(1): 87-106, 2016.
Article in English | MEDLINE | ID: mdl-26890908

ABSTRACT

This study proposes a novel geometrical force constraint method for 3-D vasculature modeling and angiographic image simulation. For this method, space filling force, gravitational force, and topological preserving force are proposed and combined for the optimization of the topology of the vascular structure. The surface covering force and surface adhesion force are constructed to drive the growth of the vasculature on any surface. According to the combination effects of the topological and surface adhering forces, a realistic vasculature can be effectively simulated on any surface. The image projection of the generated 3-D vascular structures is simulated according to the perspective projection and energy attenuation principles of X-rays. Finally, the simulated projection vasculature is fused with a predefined angiographic mask image to generate a realistic angiogram. The proposed method is evaluated on a CT image and three generally utilized surfaces. The results fully demonstrate the effectiveness and robustness of the proposed method.


Subject(s)
Algorithms , Computed Tomography Angiography/methods , Imaging, Three-Dimensional/methods , Models, Cardiovascular , Computer Simulation , Humans
7.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 33(2): 308-14, 2016 Apr.
Article in Zh | MEDLINE | ID: mdl-29708665

ABSTRACT

For tooth segmentation problem on the three-dimensional computed tomography(CT)volume data,this paper proposes a regional adaptive deformable model for tooth structure measurement of CT images.The proposed method combines the automatic thresholding segmentation,CV active contour model,and graph-cut.Firstly,we achieved the segmentation and location of dental crowns by automatic thresholding segmentation.And then by using the above segmentation result as the initial contour,we utilized active contour method to slice gradually the segment of remaining tooth.By incorporating active contour and graph-cut then,we realized the accurate segmentation for tooth root,which is the most difficult to be segmented.The experimental results showed that the proposed tooth structure measurement accurately and automatically segmented dental crowns from CT data,and then rapidly and accurately segmented the tooth neck and tooth root.The structure of tooth could be effectively segmented from CT data by using the proposed method.Experimental results indicated that the proposed method was rather robust and accurate,and could effectively assist the doctor for diagnosis in clinical treatment.


Subject(s)
Image Processing, Computer-Assisted , Tomography, X-Ray Computed , Tooth/diagnostic imaging , Algorithms , Humans
8.
Biomed Eng Online ; 14: 2, 2015 Jan 09.
Article in English | MEDLINE | ID: mdl-25572487

ABSTRACT

BACKGROUND: Magnetic resonance imaging (MRI) is corrupted by Rician noise, which is image dependent and computed from both real and imaginary images. Rician noise makes image-based quantitative measurement difficult. The non-local means (NLM) filter has been proven to be effective against additive noise. METHODS: Considering the characteristics of both Rician noise and the NLM filter, this study proposes a frame for a pre-smoothing NLM (PSNLM) filter combined with image transformation. In the PSNLM frame, noisy MRI is first transformed into an image in which noise can be treated as additive noise. Second, the transformed MRI is pre-smoothed via a traditional denoising method. Third, the NLM filter is applied to the transformed MRI, with weights that are computed from the pre-smoothed image. Finally, inverse transformation is performed on the denoised MRI to obtain the denoising results. RESULTS: To test the performance of the proposed method, both simulated and real patient data are used, and various pre-smoothing (Gaussian, median, and anisotropic filters) and image transformation [squared magnitude of the MRI, and forward and inverse variance-stabilizing trans-formations (VST)] methods are used to reduce noise. The performance of the proposed method is evaluated through visual inspection and quantitative comparison of the peak signal-to-noise ratio of the simulated data. The real data include Alzheimer's disease patients and normal controls. For the real patient data, the performance of the proposed method is evaluated by detecting atrophy regions in the hippocampus and the parahippocampal gyrus. CONCLUSIONS: The comparison of the experimental results demonstrates that using a Gaussian pre-smoothing filter and VST produce the best results for the peak signal-to-noise ratio (PSNR) and atrophy detection.


Subject(s)
Algorithms , Brain , Image Enhancement/methods , Magnetic Resonance Imaging , Signal-To-Noise Ratio , Alzheimer Disease/diagnosis , Humans , Normal Distribution
9.
J Xray Sci Technol ; 23(2): 253-65, 2015.
Article in English | MEDLINE | ID: mdl-25882735

ABSTRACT

This study proposes a novel hierarchical pyramid strategy for 3D registration of multimodality medical images. The surfaces of the source and target volume data are first extracted, and the surface point clouds are then aligned roughly using convex hull matching. The convex hull matching registration procedure could align images with large-scale transformations. The original images are divided into blocks and the corresponding blocks in the two images are registered by affine and non-rigid registration procedures. The sub-blocks are iteratively smoothed by the Gaussian kernel with different sizes during the registration procedure. The registration result of the large kernel is taken as the input of the small kernel registration. The fine registration of the two volume data sets is achieved by iteratively increasing the number of blocks, in which increase in similarity measure is taken as a criterion for acceptation of each iteration level. Results demonstrate the effectiveness and robustness of the proposed method in registering the multiple modalities of medical images.


Subject(s)
Imaging, Three-Dimensional/methods , Multimodal Imaging/methods , Algorithms , Brain/diagnostic imaging , Humans , Magnetic Resonance Imaging , Tomography, X-Ray Computed
10.
Biomed Eng Online ; 13: 112, 2014 Aug 05.
Article in English | MEDLINE | ID: mdl-25096917

ABSTRACT

BACKGROUND: Ultrasound images are usually affected by speckle noise, which is a type of random multiplicative noise. Thus, reducing speckle and improving image visual quality are vital to obtaining better diagnosis. METHOD: In this paper, a novel noise reduction method for medical ultrasound images, called multiresolution generalized N dimension PCA (MR-GND-PCA), is presented. In this method, the Gaussian pyramid and multiscale image stacks on each level are built first. GND-PCA as a multilinear subspace learning method is used for denoising. Each level is combined to achieve the final denoised image based on Laplacian pyramids. RESULTS: The proposed method is tested with synthetically speckled and real ultrasound images, and quality evaluation metrics, including MSE, SNR and PSNR, are used to evaluate its performance. CONCLUSION: Experimental results show that the proposed method achieved the lowest noise interference and improved image quality by reducing noise and preserving the structure. Our method is also robust for the image with a much higher level of speckle noise. For clinical images, the results show that MR-GND-PCA can reduce speckle and preserve resolvable details.


Subject(s)
Artifacts , Image Processing, Computer-Assisted/methods , Principal Component Analysis/methods , Algorithms , Models, Theoretical , Ultrasonics
11.
IEEE J Biomed Health Inform ; 28(10): 6064-6077, 2024 Oct.
Article in English | MEDLINE | ID: mdl-38954568

ABSTRACT

Deep learning methods have recently achieved remarkable performance in vessel segmentation applications, yet require numerous labor-intensive labeled data. To alleviate the requirement of manual annotation, transfer learning methods can potentially be used to acquire the related knowledge of tubular structures from public large-scale labeled vessel datasets for target vessel segmentation in other anatomic sites of the human body. However, the cross-anatomy domain shift is a challenging task due to the formidable discrepancy among various vessel structures in different anatomies, resulting in the limited performance of transfer learning. Therefore, we propose a cross-anatomy transfer learning framework for 3D vessel segmentation, which first generates a pre-trained model on a public hepatic vessel dataset and then adaptively fine-tunes our target segmentation network initialized from the model for segmentation of other anatomic vessels. In the framework, the adaptive fine-tuning strategy is presented to dynamically decide on the frozen or fine-tuned filters of the target network for each input sample with a proxy network. Moreover, we develop a Gaussian-based signed distance map that explicitly encodes vessel-specific shape context. The prediction of the map is added as an auxiliary task in the segmentation network to capture geometry-aware knowledge in the fine-tuning. We demonstrate the effectiveness of our method through extensive experiments on two small-scale datasets of coronary artery and brain vessel. The results indicate the proposed method effectively overcomes the discrepancy of cross-anatomy domain shift to achieve accurate vessel segmentation for these two datasets.


Subject(s)
Deep Learning , Imaging, Three-Dimensional , Humans , Imaging, Three-Dimensional/methods , Algorithms , Databases, Factual , Blood Vessels/diagnostic imaging , Blood Vessels/anatomy & histology
12.
Article in English | MEDLINE | ID: mdl-39024088

ABSTRACT

Detecting coronary stenosis accurately in X-ray angiography (XRA) is important for diagnosing and treating coronary artery disease (CAD). However, challenges arise from factors like breathing and heart motion, poor imaging quality, and the complex vascular structures, making it difficult to identify stenosis fast and precisely. In this study, we proposed a Quantum Diffusion Model with Spatio-Temporal Feature Sharing to Real-time detect Stenosis (STQD-Det). Our framework consists of two modules: Sequential Quantum Noise Boxes module and spatio-temporal feature module. To evaluate the effectiveness of the method, we conducted a 4-fold cross-validation using a dataset consisting of 233 XRA sequences. Our approach achieved the F1 score of 92.39% with a real-time processing speed of 25.08 frames per second. These results outperform 17 state-of-the-art methods. The experimental results show that the proposed method can accomplish the stenosis detection quickly and accurately.

13.
Med Phys ; 2024 Jun 12.
Article in English | MEDLINE | ID: mdl-38865713

ABSTRACT

BACKGROUND: Inferring the shape and position of coronary artery poses challenges when using fluoroscopic image guidance during percutaneous coronary intervention (PCI) procedure. Although angiography enables coronary artery visualization, the use of injected contrast agent raises concerns about radiation exposure and the risk of contrast-induced nephropathy. To address these issues, dynamic coronary roadmapping overlaid on fluoroscopic images can provide coronary visual feedback without contrast injection. PURPOSE: This paper proposes a novel cardio-respiratory motion compensation method that utilizes cardiac state synchronization and catheter motion estimation to achieve coronary roadmapping in fluoroscopic images. METHODS: For more accurate cardiac state synchronization, video frame interpolation is applied to increase the frame rate of the original limited angiographic images, resulting in higher framerate and more adequate roadmaps. The proposed method also incorporates a multi-length cross-correlation based adaptive electrocardiogram (ECG) matching to address irregular cardiac motion situation. Furthermore, a shape-constrained path searching method is proposed to extract catheter structure from both fluoroscopic and angiographic image. Then catheter motion is estimated using a cascaded matching approach with an outlier removal strategy, leading to a final corrected roadmap. RESULTS: Evaluation of the proposed method on clinical x-ray images demonstrates its effectiveness, achieving a 92.8% F1 score for catheter extraction on 589 fluoroscopic and angiographic images. Additionally, the method achieves a 5.6-pixel distance error of the coronary roadmap on 164 intraoperative fluoroscopic images. CONCLUSIONS: Overall, the proposed method achieves accurate coronary roadmapping in fluoroscopic images and shows potential to overlay accurate coronary roadmap on fluoroscopic image in assisting PCI.

14.
IEEE Trans Image Process ; 33: 3676-3691, 2024.
Article in English | MEDLINE | ID: mdl-38837936

ABSTRACT

Medical image segmentation and registration are two fundamental and highly related tasks. However, current works focus on the mutual promotion between the two at the loss function level, ignoring the feature information generated by the encoder-decoder network during the task-specific feature mapping process and the potential inter-task feature relationship. This paper proposes a unified multi-task joint learning framework based on bi-fusion of structure and deformation at multi-scale, called BFM-Net, which simultaneously achieves the segmentation results and deformation field in a single-step estimation. BFM-Net consists of a segmentation subnetwork (SegNet), a registration subnetwork (RegNet), and the multi-task connection module (MTC). The MTC module is used to transfer the latent feature representation between segmentation and registration at multi-scale and link different tasks at the network architecture level, including the spatial attention fusion module (SAF), the multi-scale spatial attention fusion module (MSAF) and the velocity field fusion module (VFF). Extensive experiments on MR, CT and ultrasound images demonstrate the effectiveness of our approach. The MTC module can increase the Dice scores of segmentation and registration by 3.2%, 1.6%, 2.2%, and 6.2%, 4.5%, 3.0%, respectively. Compared with six state-of-the-art algorithms for segmentation and registration, BFM-Net can achieve superior performance in various modal images, fully demonstrating its effectiveness and generalization.

15.
Comput Biol Med ; 171: 108176, 2024 Mar.
Article in English | MEDLINE | ID: mdl-38401453

ABSTRACT

The segmentation of the orbit in computed tomography (CT) images plays a crucial role in facilitating the quantitative analysis of orbital decompression surgery for patients with Thyroid-associated Ophthalmopathy (TAO). However, the task of orbit segmentation, particularly in postoperative images, remains challenging due to the significant shape variation and limited amount of labeled data. In this paper, we present a two-stage semi-supervised framework for the automatic segmentation of the orbit in both preoperative and postoperative images, which consists of a pseudo-label generation stage and a semi-supervised segmentation stage. A Paired Copy-Paste strategy is concurrently introduced to proficiently amalgamate features extracted from both preoperative and postoperative images, thereby augmenting the network discriminative capability in discerning changes within orbital boundaries. More specifically, we employ a random cropping technique to transfer regions from labeled preoperative images (foreground) onto unlabeled postoperative images (background), as well as unlabeled preoperative images (foreground) onto labeled postoperative images (background). It is imperative to acknowledge that every set of preoperative and postoperative images belongs to the identical patient. The semi-supervised segmentation network (stage 2) utilizes a combination of mixed supervisory signals from pseudo labels (stage 1) and ground truth to process the two mixed images. The training and testing of the proposed method have been conducted on the CT dataset obtained from the Eye Hospital of Wenzhou Medical University. The experimental results demonstrate that the proposed method achieves a mean Dice similarity coefficient (DSC) of 91.92% with only 5% labeled data, surpassing the performance of the current state-of-the-art method by 2.4%.


Subject(s)
Hospitals , Orbit , Humans , Orbit/diagnostic imaging , Orbit/surgery , Tomography, X-Ray Computed , Universities , Image Processing, Computer-Assisted
16.
Comput Biol Med ; 169: 107850, 2024 Feb.
Article in English | MEDLINE | ID: mdl-38145602

ABSTRACT

BACKGROUND: Monocular depth estimation plays a fundamental role in clinical endoscopy surgery. However, the coherent illumination, smooth surfaces, and texture-less nature of endoscopy images present significant challenges to traditional depth estimation methods. Existing approaches struggle to accurately perceive depth in such settings. METHOD: To overcome these challenges, this paper proposes a novel multi-scale residual fusion method for estimating the depth of monocular endoscopy images. Specifically, we address the issue of coherent illumination by leveraging image frequency domain component space transformation, thereby enhancing the stability of the scene's light source. Moreover, we employ an image radiation intensity attenuation model to estimate the initial depth map. Finally, to refine the accuracy of depth estimation, we utilize a multi-scale residual fusion optimization technique. RESULTS: To evaluate the performance of our proposed method, extensive experiments were conducted on public datasets. The structural similarity measures for continuous frames in three distinct clinical data scenes reached impressive values of 0.94, 0.82, and 0.84, respectively. These results demonstrate the effectiveness of our approach in capturing the intricate details of endoscopy images. Furthermore, the depth estimation accuracy achieved remarkable levels of 89.3 % and 91.2 % for the two models' data, respectively, underscoring the robustness of our method. CONCLUSIONS: Overall, the promising results obtained on public datasets highlight the significant potential of our method for clinical applications, facilitating reliable depth estimation and enhancing the quality of endoscopy surgical procedures.


Subject(s)
Endoscopy, Gastrointestinal , Endoscopy
17.
Comput Biol Med ; 169: 107890, 2024 Feb.
Article in English | MEDLINE | ID: mdl-38168646

ABSTRACT

Feature matching of monocular laparoscopic videos is crucial for visualization enhancement in computer-assisted surgery, and the keys to conducting high-quality matches are accurate homography estimation, relative pose estimation, as well as sufficient matches and fast calculation. However, limited by various monocular laparoscopic imaging characteristics such as highlight noises, motion blur, texture interference and illumination variation, most exiting feature matching methods face the challenges of producing high-quality matches efficiently and sufficiently. To overcome these limitations, this paper presents a novel sequential coupling feature descriptor to extract and express multilevel feature maps efficiently, and a dual-correlate optimized coarse-fine strategy to establish dense matches in coarse level and adjust pixel-wise matches in fine level. Firstly, a novel sequential coupling swin transformer layer is designed in feature descriptor to learn and extract multilevel feature representations richly without increasing complexity. Then, a dual-correlate optimized coarse-fine strategy is proposed to match coarse feature sequences under low resolution, and the correlated fine feature sequences is optimized to refine pixel-wise matches based on coarse matching priors. Finally, the sequential coupling feature descriptor and dual-correlate optimization are merged into the Sequential Coupling Dual-Correlate Network (SeCo DC-Net) to produce high-quality matches. The evaluation is conducted on two public laparoscopic datasets: Scared and EndoSLAM, and the experimental results show the proposed network outperforms state-of-the-art methods in homography estimation, relative pose estimation, reprojection error, matching pairs number and inference runtime. The source code is publicly available at https://github.com/Iheckzza/FeatureMatching.


Subject(s)
Laparoscopy , Surgery, Computer-Assisted , Learning , Software
18.
Comput Biol Med ; 168: 107718, 2024 01.
Article in English | MEDLINE | ID: mdl-37988787

ABSTRACT

Fractional flow reserve (FFR) is considered as the gold standard for diagnosing coronary myocardial ischemia. Existing 3D computational fluid dynamics (CFD) methods attempt to predict FFR noninvasively using coronary computed tomography angiography (CTA). However, the accuracy and efficiency of the 3D CFD methods in coronary arteries are considerably limited. In this work, we introduce a multi-dimensional CFD framework that improves the accuracy of FFR prediction by estimating 0D patient-specific boundary conditions, and increases the efficiency by generating 3D initial conditions. The multi-dimensional CFD models contain the 3D vascular model for coronary simulation, the 1D vascular model for iterative optimization, and the 0D vascular model for boundary conditions expression. To improve the accuracy, we utilize clinical parameters to derive 0D patient-specific boundary conditions with an optimization algorithm. To improve the efficiency, we evaluate the convergence state using the 1D vascular model and obtain the convergence parameters to generate appropriate 3D initial conditions. The 0D patient-specific boundary conditions and the 3D initial conditions are used to predict FFR (FFRC). We conducted a retrospective study involving 40 patients (61 diseased vessels) with invasive FFR and their corresponding CTA images. The results demonstrate that the FFRC and the invasive FFR have a strong linear correlation (r = 0.80, p < 0.001) and high consistency (mean difference: 0.014 ±0.071). After applying the cut-off value of FFR (0.8), the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of FFRC were 88.5%, 93.3%, 83.9%, 84.8%, and 92.9%, respectively. Compared with the conventional zero initial conditions method, our method improves prediction efficiency by 71.3% per case. Therefore, our multi-dimensional CFD framework is capable of improving the accuracy and efficiency of FFR prediction significantly.


Subject(s)
Coronary Artery Disease , Coronary Stenosis , Fractional Flow Reserve, Myocardial , Myocardial Ischemia , Humans , Retrospective Studies , Hydrodynamics , Coronary Angiography/methods , Coronary Artery Disease/diagnostic imaging , Predictive Value of Tests , Coronary Vessels/diagnostic imaging
19.
IEEE Trans Med Imaging ; PP2024 May 28.
Article in English | MEDLINE | ID: mdl-38805326

ABSTRACT

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.

20.
Comput Biol Med ; 169: 107766, 2024 Feb.
Article in English | MEDLINE | ID: mdl-38150885

ABSTRACT

Automatic vessel segmentation is a critical area of research in medical image analysis, as it can greatly assist doctors in accurately and efficiently diagnosing vascular diseases. However, accurately extracting the complete vessel structure from images remains a challenge due to issues such as uneven contrast and background noise. Existing methods primarily focus on segmenting individual pixels and often fail to consider vessel features and morphology. As a result, these methods often produce fragmented results and misidentify vessel-like background noise, leading to missing and outlier points in the overall segmentation. To address these issues, this paper proposes a novel approach called the progressive edge information aggregation network for vessel segmentation (PEA-Net). The proposed method consists of several key components. First, a dual-stream receptive field encoder (DRE) is introduced to preserve fine structural features and mitigate false positive predictions caused by background noise. This is achieved by combining vessel morphological features obtained from different receptive field sizes. Second, a progressive complementary fusion (PCF) module is designed to enhance fine vessel detection and improve connectivity. This module complements the decoding path by combining features from previous iterations and the DRE, incorporating nonsalient information. Additionally, segmentation-edge decoupling enhancement (SDE) modules are employed as decoders to integrate upsampling features with nonsalient information provided by the PCF. This integration enhances both edge and segmentation information. The features in the skip connection and decoding path are iteratively updated to progressively aggregate fine structure information, thereby optimizing segmentation results and reducing topological disconnections. Experimental results on multiple datasets demonstrate that the proposed PEA-Net model and strategy achieve optimal performance in both pixel-level and topology-level metrics.


Subject(s)
Benchmarking , Pisum sativum , Image Processing, Computer-Assisted
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