Accurate Vessel Segmentation Research Articles

TCDDU-Net: combining transformer and convolutional dual-path decoding U-Net for retinal vessel segmentation

Accurate segmentation of retinal blood vessels is crucial for enhancing diagnostic efficiency and preventing disease progression. However, the small size and complex structure of retinal blood vessels, coupled with low contrast in corresponding fundus images, pose significant challenges for this task. We propose a novel approach for retinal vessel segmentation, which combines the transformer and convolutional dual-path decoding U-Net (TCDDU-Net). We propose the selective dense connection swin transformer block, which converts the input feature map into patches, introduces MLPs to generate probabilities, and performs selective fusion at different stages. This structure forms a dense connection framework, enabling the capture of long-distance dependencies and effective fusion of features across different stages. The subsequent stage involves the design of the background decoder, which utilizes deformable convolution to learn the background information of retinal vessels by treating them as segmentation objects. This is then combined with the foreground decoder to form a dual-path decoding U-Net. Finally, the foreground segmentation results and the processed background segmentation results are fused to obtain the final retinal vessel segmentation map. To evaluate the effectiveness of our method, we performed experiments on the DRIVE, STARE, and CHASE datasets for retinal vessel segmentation. Experimental results show that the segmentation accuracies of our algorithms are 96.98, 97.40, and 97.23, and the AUC metrics are 98.68, 98.56, and 98.50, respectively.In addition, we evaluated our methods using F1 score, specificity, and sensitivity metrics. Through a comparative analysis, we found that our proposed TCDDU-Net method effectively improves retinal vessel segmentation performance and achieves impressive results on multiple datasets compared to existing methods.

AngioPy: an open-source, deep learning-driven tool for coronary artery segmentation

Abstract Background Quantitative coronary angiography (QCA) permits the objective assessment of coronary artery disease. However, traditional edge detection algorithms often struggle with accurate vessel segmentation due to the complexity and variable quality of angiographic images. As a result, manual correction is frequently needed, which is a major limitation of current QCA systems. Purpose We developed angioPy, a deep learning-driven tool for vessel segmentation that employs user-defined ground truth points to minimise manual correction. We compared its performance without correction with an established QCA system. Methods angioPy was developed using 2455 images from the Fractional Flow Reserve versus Angiography for Multivessel Evaluation 2 (FAME 2) study. Deep learning models were constructed using the U-Net architecture with one of three popular backbones for image classification: ResNet101, DenseNet121, and InceptionResNet-v2. Critically, the model was constructed to integrate the user’s clinical expertise through the selection of a 5-10 ground-truth points along the length of the vessel of interest including the desired start and end point of vessel segmentation. These ground truth points were added to a separate channel of the input image. Five-fold cross validation was performed using proportions of 3:1:1 for training, validation, and test sets, respectively. External validation was performed on a separate dataset of 580 images not used for model development. For both datasets, ground truth segmentation masks were provided by interventional cardiologists using specialised in-house software. A separate analysis was performed in a cohort of 74 patients with mild/moderate stenoses that underwent vessel segmentation using both angioPy and an established commercial QCA package (Medis QFR®). Vessel dimensions at stenoses (proximal and distal reference, minimal luminal diameter) were extracted from both segmentation approaches and compared (609 diameters in total). Results The best performing model performed segmentation with an average F1 score of 0.927 (pixel accuracy 0.998, precision 0.925, sensitivity 0.930, specificity 0.999) with 99.2% of masks exhibiting an F1 score > 0.8 (Figure 1). Simliar results were achieved in the external validation set (F1 score 0.924, pixel accuracy 0.997, precision 0.921, sensitivity 0.929, specificity 0.999). Stenosis dimensions measured with angioPy (mean diameter 3.54 ±1.15 mm) exhibited excellent agreement with QCA (r=0.95 [95% CI 0.95-0.96], p<0.001; mean difference -0.18 mm [limits of agreement: -0.84 to 0.49]) (Figure 2). Minimal luminal diameter (mean 2.93 ±0.91 mm) also exhibited strong agreement (r=0.93 [95% CI 0.91-0.95], p<0.001; mean difference -0.06 mm [limits of agreement: -0.70 to 0.59]. Conclusion angioPy performs rapid and accurate coronary segmentation without the need for manual correction. Available as an open-source tool, angioPy has the potential to increase the efficiency and reliability of QCA.Figure 1.angioPy segmentationFigure 2.angioPy vs QCA (Medis)

Deep Learning-Based Liver Vessel Segmentation

Abstract Liver vessel segmentation in computed tomography represents a highly challenging task due to the imbalanced distribution within the liver parenchyma, the small and branched vessels with decreased image contrast to surrounding tissue and in general, due to the scarcity of highresolution and -contrast images, which hampers the efficient training of deep learning-based approaches. This study applies two state-of-the-art networks, the fully convolutional nnUnet and the transformer-based VT-Unet to three publicly available datasets, 3DIRCADb, one task of the Medical Segmentation Decathlon (MSD) and the more recent LiVS dataset. The nnUnet achieved Dice scores of 0.761, 0.714, and 0.696 on the 3DIRCADb, LiVS, and MSD datasets, respectively. In contrast, the experiments with the VT-UNet resulted in Dice scores of 0.795, 0.713, and 0.610. These findings indicates good accordance of the performance of the nnUnet and the transformer-based VT-Unet, with differences regarding individual datasets. Both network variants show competitive performances regarding the current state-of-the-art, yet the need for large-scale and high-quality datasets becomes evident to further enhance the accuracy of liver vessel segmentation.

Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome

Twin-to-Twin Transfusion Syndrome (TTTS) is a rare condition that affects about 15% of monochorionic pregnancies, in which identical twins share a single placenta. Fetoscopic laser photocoagulation (FLP) is the standard treatment for TTTS, which significantly improves the survival of fetuses. The aim of FLP is to identify abnormal connections between blood vessels and to laser ablate them in order to equalize blood supply to both fetuses. However, performing fetoscopic surgery is challenging due to limited visibility, a narrow field of view, and significant variability among patients and domains. In order to enhance the visualization of placental vessels during surgery, we propose TTTSNet, a network architecture designed for real-time and accurate placental vessel segmentation. Our network architecture incorporates a novel channel attention module and multi-scale feature fusion module to precisely segment tiny placental vessels. To address the challenges posed by FLP-specific fiberscope and amniotic sac-based artifacts, we employed novel data augmentation techniques. These techniques simulate various artifacts, including laser pointer, amniotic sac particles, and structural and optical fiber artifacts. By incorporating these simulated artifacts during training, our network architecture demonstrated robust generalizability. We trained TTTSNet on a publicly available dataset of 2060 video frames from 18 independent fetoscopic procedures and evaluated it on a multi-center external dataset of 24 in-vivo procedures with a total of 2348 video frames. Our method achieved significant performance improvements compared to state-of-the-art methods, with a mean Intersection over Union of 78.26% for all placental vessels and 73.35% for a subset of tiny placental vessels. Moreover, our method achieved 172 and 152 frames per second on an A100 GPU, and Clara AGX, respectively. This potentially opens the door to real-time application during surgical procedures. The code is publicly available at https://github.com/SanoScience/TTTSNet.

Pretrained subtraction and segmentation model for coronary angiograms

This study introduces a novel self-supervised learning method for single-frame subtraction and vessel segmentation in coronary angiography, addressing the scarcity of annotated medical samples in AI applications. We pretrain a U-Net model on a large dataset of unannotated coronary angiograms using an image-to-image translation framework, then fine-tune it on a limited set of manually annotated samples. The pretrained model excels at comprehensive single-frame subtraction, outperforming existing DSA methods. Fine-tuning with just 40 samples yields a Dice coefficient of 0.828 for vessel segmentation. On the public XCAD dataset, our model sets a new state-of-the-art benchmark with a Dice coefficient of 0.755, surpassing both unsupervised and supervised learning approaches. This method achieves robust single-frame subtraction and demonstrates that combining pretraining with minimal fine-tuning enables accurate coronary vessel segmentation with limited manual annotations. We successfully apply this approach to assist physicians in visualizing potential vascular stenosis sites during coronary angiography. Code, dataset, and a live demo will be available available at: https://github.com/newfyu/DeepSA.

Liver vessel MRI image segmentation based on dual-path diffusion model

Accurate segmentation of liver blood vessels in magnetic resonance imaging (MRI) images is a challenging task due to the complex tree-like structure and anisotropic diffusion properties of blood vessels. To solve this problem, we propose a new Dual-Path Diffusion Model (DPDM) framework. The framework consists of two collaborative diffusion paths: a local feature learning path based on convolution operations and a global context modeling path based on transform blocks. Local path encodes rich shape priors and preserve spatial details, while global path captures long-distance dependencies and enhance representation. In the decoding phase, the boundary features from the boundary path are fused with the features of the ordinary path decoding, which further enhances the shape sensitivity. In addition, we leverage a multi-task learning scheme to jointly optimize vascular segmentation and boundary prediction tasks in an end-to-end manner. Experiments on retrospective clinical dataset demonstrate that the proposed DPDM framework achieves excellent performance on the liver vessel segmentation task. Compared with state-of-the-art methods, our approach achieved a 6.0% and 7.3% performance improvement in Dice coefficient and IoU index, respectively. Our approach offers a promising solution for automated blood vessel segmentation in precision medicine.

A multi-modal and multi-stage fusion enhancement network for segmentation based on OCT and OCTA images

The accurate segmentation of retinal vessels (RV) and Foveal Avascular Zone (FAZ) is crucial for the retina health assessment. However, fusing OCT and OCTA images while projecting these 3D data for segmentation is a challenge. In this paper, we propose an Adaptive Projection and Multi-stage Fusion Enhancement Network for the segmentation of RV and FAZ, which consists of 3D projection, fusion enhancement, and 2D segmentation parts. We design a kernel adaptive unidirectional convolution to improve projection efficiency and a volume fusion module to capture semantic and spatial information accurately. Then we construct a Multi-stage Complementary Fusion Module and a Multi-modal Difference Fusion Module to enhance features by complementarity and differences in features. At last, we employ three U-Nets with a Feature Interaction Module for segmentation. Experiments show that our method achieves state-of-the-art performance with DICE, BACC, and JAC of 0.8996, 0.8181, 0.9421, 0.9395, 0.8923, and 0.9717 for RV and FAZ.

A Novel Single-Sample Retinal Vessel Segmentation Method Based on Grey Relational Analysis.

Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of "few data, poor information", this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods.

Unsupervised domain adaptation multi-level adversarial learning-based crossing-domain retinal vessel segmentation

BackgroundThe retinal vasculature, a crucial component of the human body, mirrors various illnesses such as cardiovascular disease, glaucoma, and retinopathy. Accurate segmentation of retinal vessels in funduscopic images is essential for diagnosing and understanding these conditions. However, existing segmentation models often struggle with images from different sources, making accurate segmentation in crossing-source fundus images challenging. MethodsTo address the crossing-source segmentation issues, this paper proposes a novel Multi-level Adversarial Learning and Pseudo-label Denoising-based Self-training Framework (MLAL&PDSF). Expanding on our previously proposed Multiscale Context Gating with Breakpoint and Spatial Dual Attention Network (MCG&BSA-Net), MLAL&PDSF introduces a multi-level adversarial network that operates at both the feature and image layers to align distributions between the target and source domains. Additionally, it employs a distance comparison technique to refine pseudo-labels generated during the self-training process. By comparing the distance between the pseudo-labels and the network predictions, the framework identifies and corrects inaccuracies, thus enhancing the accuracy of the fine vessel segmentation. ResultsWe have conducted extensive validation and comparative experiments on the CHASEDB1, STARE, and HRF datasets to evaluate the efficacy of the MLAL&PDSF. The evaluation metrics included the area under the operating characteristic curve (AUC), sensitivity (SE), specificity (SP), accuracy (ACC), and balanced F-score (F1). The performance results from unsupervised domain adaptive segmentation are remarkable: for DRIVE to CHASEDB1, results are AUC: 0.9806, SE: 0.7400, SP: 0.9737, ACC: 0.9874, and F1: 0.8851; for DRIVE to STARE, results are AUC: 0.9827, SE: 0.7944, SP: 0.9651, ACC: 0.9826, and F1: 0.8326. ConclusionThese results demonstrate the effectiveness and robustness of MLAL&PDSF in achieving accurate segmentation results from crossing-domain retinal vessel datasets. The framework lays a solid foundation for further advancements in cross-domain segmentation and enhances the diagnosis and understanding of related diseases.

Partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation

Accurate segmentation of retinal vessels in fundus images is of great importance for the diagnosis of numerous ocular diseases. However, due to the complex characteristics of fundus images, such as various lesions, image noise and complex background, the pixel features of some vessels have significant differences, which makes it easy for the segmentation networks to misjudge these vessels as noise, thus affecting the accuracy of the overall segmentation. Therefore, accurately segment retinal vessels in complex situations is still a great challenge. To address the problem, a partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation is proposed. The core idea of the proposed network is first to use the partial class activation mapping guided graph convolutional network to eliminate the differences of local vessels and generate feature maps with global consistency, and subsequently these feature maps are further refined by segmentation network U-Net to achieve better segmentation results. Specifically, a new neural network block, called EdgeConv, is stacked multiple layers to form a graph convolutional network to realize the transfer an update of information from local to global, so as gradually enhance the feature consistency of graph nodes. Simultaneously, in an effort to suppress the noise information that may be transferred in graph convolution and thus reduce adverse effects of noise on segmentation results, the partial class activation mapping is introduced. The partial class activation mapping can guide the information transmission between graph nodes and effectively activate vessel feature through classification labels, thereby improving the accuracy of segmentation. The performance of the proposed method is validated on four different fundus image datasets. Compared with existing state-of-the-art methods, the proposed method can improve the integrity of vessel to a certain extent when the pixel features of local vessels are significantly different, caused by objective factors such as inappropriate illumination and exudates. Moreover, the proposed method shows robustness when segmenting complex retinal vessels.

Compensation of small data with large filters for accurate liver vessel segmentation from contrast-enhanced CT images

BackgroundSegmenting liver vessels from contrast-enhanced computed tomography images is essential for diagnosing liver diseases, planning surgeries and delivering radiotherapy. Nevertheless, identifying vessels is a challenging task due to the tiny cross-sectional areas occupied by vessels, which has posed great challenges for vessel segmentation, such as limited features to be learned and difficult to construct high-quality as well as large-volume data.MethodsWe present an approach that only requires a few labeled vessels but delivers significantly improved results. Our model starts with vessel enhancement by fading out liver intensity and generates candidate vessels by a classifier fed with a large number of image filters. Afterwards, the initial segmentation is refined using Markov random fields.ResultsIn experiments on the well-known dataset 3D-IRCADb, the averaged Dice coefficient is lifted to 0.63, and the mean sensitivity is increased to 0.71. These results are significantly better than those obtained from existing machine-learning approaches and comparable to those generated from deep-learning models.ConclusionSophisticated integration of a large number of filters is able to pinpoint effective features from liver images that are sufficient to distinguish vessels from other liver tissues under a scarcity of large-volume labeled data. The study can shed light on medical image segmentation, especially for those without sufficient data.

Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm.

CT-image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time-consuming process and inter-observer variations of manual segmentation have limited wider application in clinical practice. Our study aimed to propose an automated deep learning (DL) segmentation algorithm for liver and hepatic vessels on portal venous phase CT images. This retrospective study was performed to develop a coarse-to-fine DL-based algorithm that was trained, validated, and tested using private 413, 52, and 50 portal venous phase CT images, respectively. Additionally, the performance of the DL algorithm was extensively evaluated and compared with manual segmentation using an independent clinical dataset of preoperative contrast-enhanced CT images from 44 patients with hepatic focal lesions. The accuracy of DL-based segmentation was quantitatively evaluated using the Dice Similarity Coefficient (DSC) and complementary metrics [Normalized Surface Dice (NSD) and Hausdorff distance_95 (HD95) for liver segmentation, Recall and Precision for hepatic vessel segmentation]. The processing time for DL and manual segmentation was also compared. Our DL algorithm achieved accurate liver segmentation with DSC of 0.98, NSD of 0.92, and HD95 of 1.52mm. DL-segmentation of hepatic veins, portal veins, and inferior vena cava attained DSC of 0.86, 0.89, and 0.94, respectively. Compared with the manual approach, the DL algorithm significantly outperformed with better segmentation results for both liver and hepatic vessels, with higher accuracy of liver and hepatic vessel segmentation (all p<0.001) in independent 44 clinical data. In addition, the DL method significantly reduced the manual processing time of clinical postprocessing (p<0.001). The proposed DL algorithm potentially enabled accurate and rapid segmentation for liver and hepatic vessels using portal venous phase contrast CT images.

HiDiffSeg: A hierarchical diffusion model for blood vessel segmentation in retinal fundus images

In medical diagnosis, achieving accurate segmentation of fundus vessels is crucial for understanding and identifying various anatomical structures. However, traditional algorithms often face challenges in achieving precise segmentation due to the poor quality of fundus images and the complex branching structure of vessels. To address this issue, we propose a novel approach called HiDiffSeg, which is a coarse-to-fine hierarchical diffusion model for blood vessel segmentation. Our method integrates an enhanced processing coarse-to-fine strategy for retinal fundus images into the denoising process of the diffusion model. The diffusion model is divided into two modules: the dual-guidance module (DGM) and the refinement-guidance module (RGM). The DGM takes the vascular skeleton image and the initial denoised vessel segmentation image generated by the vascular image enhancement model as conditions. Meanwhile, the RGM uses the fundus image as a condition to obtain more accurate results through iterative refinement. To emphasize the significance of vessel edges, we introduce a vessel enhancement module. By taking the original image as input, we generate a pixel-wise edge attention map, assigning greater importance to corresponding edge pixels. To the best of our knowledge, this is the first time a hierarchical diffusion model has been applied to fundus vessel segmentation. We demonstrate the accuracy of our method on three publicly available fundus retinal datasets (i.e., DRIVE, STARE, and CHASE_DB1) using evaluation metrics and compare it with eleven state-of-the-art fundus vessel segmentation methods.

Glaucoma detection using non-perfused areas in OCTA

Multiple ophthalmic diseases lead to decreased capillary perfusion that can be visualized using optical coherence tomography angiography images. To quantify the decrease in perfusion, past studies have often used the vessel density, which is the percentage of vessel pixels in the image. However, this method is often not sensitive enough to detect subtle changes in early pathology. More recent methods are based on quantifying non-perfused or intercapillary areas between the vessels. These methods rely upon the accuracy of vessel segmentation, which is a challenging task and therefore a limiting factor for reliability. Intercapillary areas computed from perfusion-distance measures are less sensitive to errors in the vessel segmentation since the distance to the next vessel is only slightly changing if gaps are present in the segmentation. We present a novel method for distinguishing between glaucoma patients and healthy controls based on features computed from the probability density function of these perfusion-distance areas. The proposed approach is evaluated on different capillary plexuses and outperforms previously proposed methods that use handcrafted features for classification. Moreover the results of the proposed method are in the same range as the ones of convolutional neural networks trained on the raw input images and is therefore a computationally efficient, simple to implement and explainable alternative to deep learning-based approaches.

Choroidal Layer Analysis in OCT images via Ambiguous Boundary-aware Attention

Optical Coherence Tomography (OCT) is a commonly used retina imaging technique, and it is capable of revealing the morphology of the choroid. However, the segmentation and quantitative analysis of the sublayers and vessels in choroid are rarely explored, primarily due to the indistinct boundaries of choroidal sublayers, and imbalanced distribution of vessels observed in OCT imagery. In this paper, we propose a novel two-stage architecture called Choroidal Layer Analysis network (CLA), that may be considered the first attempt in this research community for joint segmentation of choroidal sublayers and choroidal vessels in OCT images. CLA employs the encoder–decoder network with the residual U-shape module as the backbone. In order to empower the ability of the segmentation model to identify the inconspicuous boundaries of choroidal sublayers, we introduce an Ambiguous Boundary Attention block (ABA) into the bottleneck of the encoder–decoder network in the first stage. For more accurate segmentation of large choroidal vessels with ambiguous contours and imbalanced spatial distribution, the second stage introduces an active contour-based loss to refine the contours of choroidal vessels simultaneously with precise identification of each vessel via contextual modeling. To train, test and validate the proposed model, we conducted a choroidal segmentation dataset containing 800 OCT images, with their sublayers and large choroidal vessels manually annotated. Experimental results demonstrate the superiority of the proposed approach compared with other state-of-the-art segmentation networks in large margins. It is worth noting that we also reconstructed the large choroidal vessels in three-dimensional (3D) based on the segmentation results, and multiple 3D morphological parameters were calculated. The statistical analysis of these parameters demonstrates significant differences between the healthy control and high myopia group, and this further confirms the proposed work may facilitate subsequent disease understanding and clinical decision-making.

FRD-Net: a full-resolution dilated convolution network for retinal vessel segmentation

Accurate and automated retinal vessel segmentation is essential for performing diagnosis and surgical planning of retinal diseases. However, conventional U-shaped networks often suffer from segmentation errors when dealing with fine and low-contrast blood vessels due to the loss of continuous resolution in the encoding stage and the inability to recover the lost information in the decoding stage. To address this issue, this paper introduces an effective full-resolution retinal vessel segmentation network, namely FRD-Net, which consists of two core components: the backbone network and the multi-scale feature fusion module (MFFM). The backbone network achieves horizontal and vertical expansion through the interaction mechanism of multi-resolution dilated convolutions while preserving the complete image resolution. In the backbone network, the effective application of dilated convolutions with varying dilation rates, coupled with the utilization of dilated residual modules for integrating multi-scale feature maps from adjacent stages, facilitates continuous learning of multi-scale features to enhance high-level contextual information. Moreover, MFFM further enhances segmentation by fusing deeper multi-scale features with the original image, facilitating edge detail recovery for accurate vessel segmentation. In tests on multiple classical datasets,compared to state-of-the-art segmentation algorithms, FRD-Net achieves superior performance and generalization with fewer model parameters.

Bi-directional ConvLSTM residual U-Net retinal vessel segmentation algorithm with improved focal loss function

Accurate blood vessel segmentation on retinal blood vessel images is helpful for the early detection of ophthalmic diseases such as diabetes, hypertension, cardiovascular and cerebrovascular diseases, and inhibits the deterioration of the disease. In current research within the field of retinal blood vessel segmentation, significant challenges exist in accurately segmenting small blood vessels and maintaining blood vessel continuity. The segmentation algorithm proposed in this article offers substantial improvements to address these issues. To enhance the segmentation performance of retinal blood vessels and facilitate more accurate diagnosis of fundus diseases by ophthalmologists, this paper introduces a novel bidirectional convolutional long short-term memory (LSTM) residual U-Net segmentation algorithm, incorporating improvements to the Focal loss function. Firstly, in the encoding part of U-Net, the multi-scale convolution kernels and Bi-ConvLSTM were adopted to improve the residual structure, obtain richer blood vessel features and enhance the detection ability of micro vessels and the continuity of blood vessel characteristics. At the same time, the class balanced cross entropy loss function was improved and the proportional modulation factor is introduced to enhance the learning ability of the network for difficult samples. By adding the Bi-ConvLSTM to the residual structure and introducing the proportional modulation coefficient to the loss function, the network structure realizes better feature information detection and greatly enhances the detection ability of small blood vessels. The experimental analysis on the DRIVE and CHASE_DB1 data sets showed that the sensitivity, specificity, accuracy and AUC reached 0.7961, 0.9796, 0.9563, 0.9792; 0.8344, 0.9665, 0.9547, 0.9758, respectively. The experimental results fully show that the Bi-ConvLSTM residual U-Net segmentation algorithm based on the improved Focal loss function enhances the detection ability of small blood vessel features, improves the continuity of blood vessel features and the network segmentation performance, and is superior to U-Net algorithm and some current mainstream retinal blood vessel segmentation algorithms.

Development and Validation of an Algorithm Model for Predicting Heat Sink Effects during Pulmonary Thermal Ablation

The aim of this study was to develop an algorithm model to predict the heat sink effect during thermal ablation of lung tumors and to assist doctors in the formulation and adjustment of surgical protocols. The heat sink effect is an important factor affecting the therapeutic effect of tumor thermal ablation. At present, there is no algorithm model to predict the intraoperative heat sink effect automatically, which needs to be measured manually, which lacks accuracy and consumes time. To construct a segmentation model based on a convolutional neural network that can automatically identify and segment pulmonary nodules and vascular structure and measure the distance between the nodule and vascular. First, the classical Faster RCNN model was used as the nodule detection network. After obtaining the bounding box of pulmonary nodules, the VSPP-NET model was used to segment nodules in the bounding box. The distance from the nodule to the vasculature was measured after the surrounding vasculature was segmented by the VSPP-NET model. The lung CT images of 392 patients with pulmonary nodules were used as the training data for the algorithm. 68 cases were used as algorithm validation data, 29 as nodule algorithm test data, and 80 as vascular algorithm test data. We compared the heat sink effect of 29 cases of data with the results of the algorithm model and expert segmentation and compared the difference between the two results. In pulmonary CT image vasculature segmentation, the recall and precision of the algorithm model reached >0.88 and >0.78, respectively. The average time for automatic segmentation of each image model is 29 seconds, and the average time for manual segmentation is 158 seconds. The output image of the model shows that the results of nodule segmentation and nodule distance measurement are satisfactory. In terms of heat sink effect prediction, the positive rate of the algorithm group was 28.3%, and that of the expert group was 32.1%, with no significant difference between the two groups (p=0.687). The algorithm model developed in this study shows good performance in predicting the heat sink effect during pulmonary thermal ablation. It can improve the speed and accuracy of nodule and vessel segmentation, save ablation planning time, reduce the interference of human factors, and provide more reference information for surgeons to make ablation plans to improve the ablation effect.

A Multi-Scale Attention Fusion Network for Retinal Vessel Segmentation

The structure and function of retinal vessels play a crucial role in diagnosing and treating various ocular and systemic diseases. Therefore, the accurate segmentation of retinal vessels is of paramount importance to assist a clinical diagnosis. U-Net has been highly praised for its outstanding performance in the field of medical image segmentation. However, with the increase in network depth, multiple pooling operations may lead to the problem of crucial information loss. Additionally, handling the insufficient processing of local context features caused by skip connections can affect the accurate segmentation of retinal vessels. To address these problems, we proposed a novel model for retinal vessel segmentation. The proposed model is implemented based on the U-Net architecture, with the addition of two blocks, namely, an MsFE block and MsAF block, between the encoder and decoder at each layer of the U-Net backbone. The MsFE block extracts low-level features from different scales, while the MsAF block performs feature fusion across various scales. Finally, the output of the MsAF block replaces the skip connection in the U-Net backbone. Experimental evaluations on the DRIVE dataset, CHASE_DB1 dataset, and STARE dataset demonstrated that MsAF-UNet exhibited excellent segmentation performance compared with the state-of-the-art methods.

Deep learning-based 3D cerebrovascular segmentation workflow on bright and black blood sequences magnetic resonance angiography

BackgroundCerebrovascular diseases have emerged as significant threats to human life and health. Effectively segmenting brain blood vessels has become a crucial scientific challenge. We aimed to develop a fully automated deep learning workflow that achieves accurate 3D segmentation of cerebral blood vessels by incorporating classic convolutional neural networks (CNNs) and transformer models.MethodsWe used a public cerebrovascular segmentation dataset (CSD) containing 45 volumes of 1.5 T time-of-flight magnetic resonance angiography images. We collected data from another private middle cerebral artery (MCA) with lenticulostriate artery (LSA) segmentation dataset (MLD), which encompassed 3.0 T three-dimensional T1-weighted sequences of volumetric isotropic turbo spin echo acquisition MRI images of 107 patients aged 62 ± 11 years (42 females). The workflow includes data analysis, preprocessing, augmentation, model training with validation, and postprocessing techniques. Brain vessels were segmented using the U-Net, V-Net, UNETR, and SwinUNETR models. The model performances were evaluated using the dice similarity coefficient (DSC), average surface distance (ASD), precision (PRE), sensitivity (SEN), and specificity (SPE).ResultsDuring 4-fold cross-validation, SwinUNETR obtained the highest DSC in each fold. On the CSD test set, SwinUNETR achieved the best DSC (0.853), PRE (0.848), SEN (0.860), and SPE (0.9996), while V-Net achieved the best ASD (0.99). On the MLD test set, SwinUNETR demonstrated good MCA segmentation performance and had the best DSC, ASD, PRE, and SPE for segmenting the LSA.ConclusionsThe workflow demonstrated excellent performance on different sequences of MRI images for vessels of varying sizes. This method allows doctors to visualize cerebrovascular structures.Critical relevance statementA deep learning-based 3D cerebrovascular segmentation workflow is feasible and promising for visualizing cerebrovascular structures and monitoring cerebral small vessels, such as lenticulostriate arteries.Key points• The proposed deep learning-based workflow performs well in cerebrovascular segmentation tasks.• Among comparison models, SwinUNETR achieved the best DSC, ASD, PRE, and SPE values in lenticulostriate artery segmentation.• The proposed workflow can be used for different MR sequences, such as bright and black blood imaging.Graphical