Medical & Biological Engineering & Computing最新文献

Automated measurement of cardiothoracic ratio based on semantic segmentation integration model using deep learning.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-21 DOI: 10.1007/s11517-024-03263-0

Jiajun Feng, Yuqian Huang, Zhenbin Hu, Junjie Guo

{"title":"Automated measurement of cardiothoracic ratio based on semantic segmentation integration model using deep learning.","authors":"Jiajun Feng, Yuqian Huang, Zhenbin Hu, Junjie Guo","doi":"10.1007/s11517-024-03263-0","DOIUrl":"https://doi.org/10.1007/s11517-024-03263-0","url":null,"abstract":"The objective of this study is to investigate the efficacy of the semantic segmentation model in predicting cardiothoracic ratio (CTR) and heart enlargement and compare its consistency with the reference standard. A total of 650 consecutive chest radiographs from our center and 756 public datasets were retrospectively included to develop a segmentation model. Three semantic segmentation models were used to segment the heart and lungs. A soft voting integration method was used to improve the segmentation accuracy and measure CTR automatically. Bland-Altman and Pearson's correlation analyses were used to compare the consistency and correlation between CTR automated measurements and reference standards. CTR automated measurements were compared with reference standard using the Wilcoxon signed-rank test. The diagnostic efficacy of the model for heart enlargement was evaluated using the AUC. The soft voting integration model was strongly correlated (r = 0.98, P < 0.001) and consistent (average standard deviation of 0.0048 cm/s) with the reference standard. No statistical difference between CTR automated measurement and reference standard in healthy subjects, pneumothorax, pleural effusion, and lung mass patients (P > 0.05). In the external test data, the accuracy, sensitivity, specificity, and AUC in determining heart enlargement were 96.0%, 79.5%, 99.1%, and 0.988, respectively. The deep learning method was calculated faster per chest radiograph than the average time manually calculated by the radiologist (about 2 s vs 25.75 ± 4.35 s, respectively, P < 0.001). This study provides a semantic segmentation integration model of chest radiographs to measure CTR and determine heart enlargement with chest structure changes due to different chest diseases effectively, faster, and accurately. The development of the automated segmentation integration model is helpful in improving the consistency of CTR measurement, reducing the workload of radiologists, and improving their work efficiency.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancement in medical report generation: current practices, challenges, and future directions.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-21 DOI: 10.1007/s11517-024-03265-y

Marwareed Rehman, Imran Shafi, Jamil Ahmad, Carlos Osorio Garcia, Alina Eugenia Pascual Barrera, Imran Ashraf

{"title":"Advancement in medical report generation: current practices, challenges, and future directions.","authors":"Marwareed Rehman, Imran Shafi, Jamil Ahmad, Carlos Osorio Garcia, Alina Eugenia Pascual Barrera, Imran Ashraf","doi":"10.1007/s11517-024-03265-y","DOIUrl":"https://doi.org/10.1007/s11517-024-03265-y","url":null,"abstract":"The correct analysis of medical images requires the medical knowledge and expertise of radiologists to understand, clarify, and explain complex patterns and diagnose diseases. After analyzing, radiologists write detailed and well-structured reports that contribute to the precise and timely diagnosis of patients. However, manually writing reports is often expensive and time-consuming, and it is difficult for radiologists to analyze medical images, particularly images with multiple views and perceptions. It is challenging to accurately diagnose diseases, and many methods are proposed to help radiologists, both traditional and deep learning-based. Automatic report generation is widely used to tackle this issue as it streamlines the process and lessens the burden of manual labeling of images. This paper introduces a systematic literature review with a focus on analyses and evaluating existing research on medical report generation. This SLR follows a proper protocol for the planning, reviewing, and reporting of the results. This review recognizes that the most commonly used deep learning models are encoder-decoder frameworks (45 articles), which provide an accuracy of around 92-95%. Transformers-based models (20 articles) are the second most established method and achieve an accuracy of around 91%. The remaining articles explored in this SLR are attention mechanisms (10), RNN-LSTM (10), Large language models (LLM-10), and graph-based methods (4) with promising results. However, these methods also face certain limitations such as overfitting, risk of bias, and high data dependency that impact their performance. The review not only highlights the strengths and challenges of these methods but also suggests ways to handle them in the future to increase the accuracy and timely generation of medical reports. The goal of this review is to direct radiologists toward methods that lessen their workload and provide precise medical diagnoses.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting hospitalization with LLMs from health insurance data.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-19 DOI: 10.1007/s11517-024-03251-4

Everton F Baro, Luiz S Oliveira, Alceu de Souza Britto

{"title":"Predicting hospitalization with LLMs from health insurance data.","authors":"Everton F Baro, Luiz S Oliveira, Alceu de Souza Britto","doi":"10.1007/s11517-024-03251-4","DOIUrl":"https://doi.org/10.1007/s11517-024-03251-4","url":null,"abstract":"Predictions of hospitalizations can help in the development of applications for health insurance, hospitals, and medicine. The data collected by health insurance has potential that is not always explored, and extracting features from it for use in machine learning applications requires demanding processes and specialized knowledge. With the emergence of large language models (LLM) there are possibilities to use this data for a wide range of applications requiring little specialized knowledge. To do this, it is necessary to organize and prepare this data to be used by these models. Therefore, in this work, an approach is presented for using data from health insurance in LLMs with the objective of predict hospitalizations. As a result, pre-trained models were generated in Portuguese and English with health insurance data that can be used in several applications. To prove the effectiveness of the models, tests were carried out to predict hospitalizations in general and due to stroke. For hospitalizations in general, F1-Score = 87.8 and AUC = 0.955 were achieved, and for hospitalizations due to stroke, the best model achieved F1-Score = 88.7 and AUC of 0.964. Considering the potential for use, the models were made available to the scientific community.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding the implication of task conditions on asymmetry in gait of post-stroke individuals using an Integrated Wearable System.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-19 DOI: 10.1007/s11517-024-03249-y

Shashi Ranjan, Priya Darji, Shraddha J Diwan, Uttama Lahiri

{"title":"Understanding the implication of task conditions on asymmetry in gait of post-stroke individuals using an Integrated Wearable System.","authors":"Shashi Ranjan, Priya Darji, Shraddha J Diwan, Uttama Lahiri","doi":"10.1007/s11517-024-03249-y","DOIUrl":"https://doi.org/10.1007/s11517-024-03249-y","url":null,"abstract":"Hemiplegic individuals often demonstrate gait abnormality causing asymmetry in lower-limb muscle activation-related (implicit) and gait-related (explicit) measures (offering complementary information on one's gait) while walking. Added to hemiplegia, such asymmetry can be aggravated while walking under varying task conditions, namely, walking without speaking (single task), walking while counting backwards (dual task), and walking while holding an object and counting backwards (multiple task). This emphasizes the need to quantify the extent of aggravated implication of multiple-task and dual-task on gait asymmetry compared to single task. Here, we used Integrated Wearable System and carried out a study with a group of age-matched hemiplegic (Grp_S) and healthy (Grp_H) individuals to understand the potential of our system in quantifying asymmetry in explicit and implicit measures of gait, implication of hemiplegic condition and varying task conditions on these asymmetry measures along with their clinical relevance. Results showed the potential of our system in quantifying asymmetry in both explicit and implicit measures of gait, and these measures were statistically higher (p-value < 0.05) in Grp_S than Grp_H irrespective of the task conditions. Also, for Grp_S, these asymmetry measures became more pronounced as task demand increased, and again, these measures have shown a correlation with their risk of fall specifically during more attention-demanding tasks that could be clinically relevant.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Survivability prognosis of lung cancer patients with comorbidities-a Gaussian Bayesian network model.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-18 DOI: 10.1007/s11517-024-03261-2

Shih-Hsien Tseng, Kung-Min Wang, Ting-Yang Su, Kung-Jeng Wang

{"title":"Survivability prognosis of lung cancer patients with comorbidities-a Gaussian Bayesian network model.","authors":"Shih-Hsien Tseng, Kung-Min Wang, Ting-Yang Su, Kung-Jeng Wang","doi":"10.1007/s11517-024-03261-2","DOIUrl":"https://doi.org/10.1007/s11517-024-03261-2","url":null,"abstract":"Comorbidities are influencing factors that cause lung cancer. An accurate survivability prediction model is required considering these confounding factors (a variety of comorbidities and treatments). The study developed a conditional Gaussian Bayesian network (CGBN) model to predict the related survival time with likelihood under various conditions. The lung cancer patients were collected from the National Health Insurance Research Database in Taiwan. Six major chronic diseases (i.e., pulmonary tuberculosis, COPD, kidney failure, diabetes mellitus, stroke, and liver disease) are investigated. A total of 2875 lung cancer cases with key comorbidities were selected. This study examined three types of lung cancer treatment: surgery, chemotherapy, and targeted therapy. The study outcomes provided the likelihood of survival time occurrences. Survival analysis indicates that diabetes mellitus and liver disease are significantly riskier than the other comorbidities for lung cancer patients. The proposed CGBN model achieved high accuracy as compared to the existing literature. The proposed CGBN model is advantageous for modeling the relationship between numerical and categorical influencing factors and response variables for lung cancer with comorbidities. The proposed model facilitates the flexible and accurate estimation of various lung cancer-related queries.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The influence of PEEK acetabular shell on the mechanical stability of total hip replacements under gait loading and motion. PEEK 髋臼壳对全髋关节置换术在步态负荷和运动下机械稳定性的影响。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-16 DOI: 10.1007/s11517-024-03257-y

Hongxing Shi, Xiaogang Zhang, Zhenxian Chen, Yali Zhang, Zhongmin Jin

{"title":"The influence of PEEK acetabular shell on the mechanical stability of total hip replacements under gait loading and motion.","authors":"Hongxing Shi, Xiaogang Zhang, Zhenxian Chen, Yali Zhang, Zhongmin Jin","doi":"10.1007/s11517-024-03257-y","DOIUrl":"https://doi.org/10.1007/s11517-024-03257-y","url":null,"abstract":"The demand for total hip replacement surgery is increasing year by year. However, the issue of hip prosthesis failure, particularly the modular acetabular cup, still exists. The performance and functional requirements of modular acetabular cups have not yet met clinical expectations. This study focused on poly-ether-ether-ketone (PEEK) shells, using finite element methods to investigate their mechanical stability under gait loads and motion, including parameters such as deformation, micromotion, and bone strain. The results showed that a compromise was required among the mechanical performance, stability, and bone integration capabilities of the PEEK shell. As the shell rigidity increased, deformation decreased. However, increased rigidity also increased micromotion at the bone-prosthesis interface, reducing the area that promoted bone ingrowth. Additionally, potential bone absorption areas were also increased, reducing bone preservation and reconstruction capabilities. Compromises need to be made among mechanical performance, stability, and bone integration to achieve optimal mechanical stability. In this study, a 6 mm wall thickness PEEK shell was found to provide good overall mechanical stability.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fully automated measurement of migration percentage on ultrasound images in children with cerebral palsy.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-15 DOI: 10.1007/s11517-024-03259-w

Reza Yousefvand, Thanh-Tu Pham, Lawrence H Le, John Andersen, Edmond Lou

{"title":"A fully automated measurement of migration percentage on ultrasound images in children with cerebral palsy.","authors":"Reza Yousefvand, Thanh-Tu Pham, Lawrence H Le, John Andersen, Edmond Lou","doi":"10.1007/s11517-024-03259-w","DOIUrl":"https://doi.org/10.1007/s11517-024-03259-w","url":null,"abstract":"Migration percentage (MP) is the gold standard to assess the severity of hip displacement in children with cerebral palsy, which is measured on anteroposterior hip radiographs. Recently, the ultrasound (US) method has been developed as a safe alternative imaging modality to image and monitor children's hips. However, measuring MP on US images (MPUS) is time-consuming, challenging, and user-dependent. This study aimed to develop machine learning algorithms to automatically measure MPUS and validate the algorithms with MPXray. A combination of signal filtering, convolution neural networks (CNNs), and UNets was applied to segment the regions of interest (ROI), detect edges or feature points, and select the desired US frames. A total of 62 hips including both coronal and transverse scans per hip were acquired, out of which 36 with applying augmentation method were utilized for training, 8 for validation, and 18 for testing. The intraclass correlation coefficient (ICC2,1) and the mean absolute difference (MAD) between the automated MPUS versus manual MPXray were 0.86 and 6.5% ± 5.5%, respectively. To report the MPUS, it took an average of 104 s/hip. This preliminary result demonstrated that MPUS was able to extract automatically within 2 min with a clinical acceptance accuracy (10%).","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pan-Ret: a semi-supervised framework for scalable detection of pan-retinal diseases. Pan-Ret：可扩展的泛视网膜疾病检测半监督框架。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-14 DOI: 10.1007/s11517-024-03250-5

Rohan Banerjee, Rakhshanda Mujib, Prayas Sanyal, Tapabrata Chakraborti, Sanjoy Kumar Saha

{"title":"Pan-Ret: a semi-supervised framework for scalable detection of pan-retinal diseases.","authors":"Rohan Banerjee, Rakhshanda Mujib, Prayas Sanyal, Tapabrata Chakraborti, Sanjoy Kumar Saha","doi":"10.1007/s11517-024-03250-5","DOIUrl":"https://doi.org/10.1007/s11517-024-03250-5","url":null,"abstract":"It has been shown in recent years that a range of optical diseases have early manifestation in retinal fundus images. It is becoming increasingly important to separate the regions of interest (RoI) upfront in the automated classification pipeline in order to ensure the alignment of the disease diagnosis with clinically relevant visual features. In this work, we introduce Pan-Ret, a semi-supervised framework which starts with locating the abnormalities in the biomedically relevant RoIs of a retinal image in an \"annotation-agnostic\" fashion. It does so by leveraging an anomaly detection setup using parallel autoencoders that are trained only on healthy population initially. Then, the anomalous images are separated based on the RoIs using a fully interpretable classifier like support vector machine (SVM). Experimental results show that the proposed approach yields an overall F1-score of 0.95 and 0.96 in detecting abnormalities on two different public datasets covering a diverse range of retinal diseases including diabetic retinopathy, hypertensive retinopathy, glaucoma, age-related macular degeneration, and several more in a staged manner. Thus, the work presents a milestone towards a pan-retinal disease diagnostic pipeline that can not only cater to the current set of disease classes, but has the capacity of adding further classes down the line. This is due to an anomaly detection style one-class learning setup of the deep autoencoder piece of the proposed pipeline, thus improving the generalizability of this approach compared to usual fully supervised competitors. This is also expected to increase the practical translational potential of Pan-Ret in a real-life scalable clinical setting.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EchoSegDiff: a diffusion-based model for left ventricular segmentation in echocardiography. EchoSegDiff：基于扩散的超声心动图左心室分割模型。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-14 DOI: 10.1007/s11517-024-03255-0

Huijuan Tian, Lei Zhang, Xuetong Fu, Hongyang Zhang, Yuanquan Wang, Shoujun Zhou, Jin Wei

{"title":"EchoSegDiff: a diffusion-based model for left ventricular segmentation in echocardiography.","authors":"Huijuan Tian, Lei Zhang, Xuetong Fu, Hongyang Zhang, Yuanquan Wang, Shoujun Zhou, Jin Wei","doi":"10.1007/s11517-024-03255-0","DOIUrl":"https://doi.org/10.1007/s11517-024-03255-0","url":null,"abstract":"Echocardiography is a primary tool for cardiac diagnosis. Accurate delineation of the left ventricle is a prerequisite for echocardiography-based clinical decision-making. In this work, we propose an echocardiographic left ventricular segmentation method based on the diffusion probability model, which is named EchoSegDiff. The EchoSegDiff takes an encoder-decoder structure in the reverse diffusion process. A diffusion encoder residual block (DEResblock) based on the atrous pyramid squeeze attention (APSA) block is coined as the main module of the encoder, so that the EchoSegDiff can catch multiscale features effectively. A novel feature fusion module (FFM) is further proposed, which can adaptively fuse the features from encoder and decoder to reduce semantic gap between encoder and decoder. The proposed EchoSegDiff is validated on two publicly available echocardiography datasets. In terms of left ventricular segmentation performance, it outperforms other state-of-the-art networks. The segmentation accuracy on the two datasets reached 93.69% and 89.95%, respectively. This demonstrates the excellent potential of EchoSegDiff in the task of left ventricular segmentation in echocardiography.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CMFNet: a cross-dimensional modal fusion network for accurate vessel segmentation based on OCTA data. CMFNet：基于 OCTA 数据进行精确血管分割的跨维模态融合网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-13 DOI: 10.1007/s11517-024-03256-z

Siqi Wang, Xiaosheng Yu, Hao Wu, Ying Wang, Chengdong Wu

{"title":"CMFNet: a cross-dimensional modal fusion network for accurate vessel segmentation based on OCTA data.","authors":"Siqi Wang, Xiaosheng Yu, Hao Wu, Ying Wang, Chengdong Wu","doi":"10.1007/s11517-024-03256-z","DOIUrl":"https://doi.org/10.1007/s11517-024-03256-z","url":null,"abstract":"Optical coherence tomography angiography (OCTA) is a novel non-invasive retinal vessel imaging technique that can display high-resolution 3D vessel structures. The quantitative analysis of retinal vessel morphology plays an important role in the automatic screening and diagnosis of fundus diseases. The existing segmentation methods struggle to effectively use the 3D volume data and 2D projection maps of OCTA images simultaneously, which leads to problems such as discontinuous microvessel segmentation results and deviation of morphological estimation. To enhance diagnostic support for fundus diseases, we propose a cross-dimensional modal fusion network (CMFNet) using both 3D volume data and 2D projection maps for accurate OCTA vessel segmentation. Firstly, we use different encoders to generate 2D projection features and 3D volume data features from projection maps and volume data, respectively. Secondly, we design an attentional cross-feature projection learning module to purify 3D volume data features and learn its projection features along the depth direction. Then, we develop a cross-dimensional hierarchical fusion module to effectively fuse coded features learned from the volume data and projection maps. In addition, we extract high-level semantic weight information and map it to the cross-dimensional hierarchical fusion process to enhance fusion performance. To validate the efficacy of our proposed method, we conducted experimental evaluations using the publicly available dataset: OCTA-500. The experimental results show that our method achieves state-of-the-art performance.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0