{"title":"Towards Improving the Saccade Angle Recognition Using the Sensitivity Weights of Channels","authors":"Jianning Hua, Qinghua Luo, Lintao Xu, Bowei Hu, Ziping Chen","doi":"10.1007/s40846-024-00894-4","DOIUrl":"https://doi.org/10.1007/s40846-024-00894-4","url":null,"abstract":"","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920971","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}
Pub Date : 2024-08-08DOI: 10.1007/s40846-024-00892-6
Weijing Xu, Z. Sha, Tao Tan, Wentao Liu, Yifu Chen, Zhanying Li, Xipeng Pan, Rongcai Jiang, Huihua Yang
{"title":"Automatic Segmentation of Intracranial Hemorrhage in Computed Tomography Scans with Convolution Neural Networks","authors":"Weijing Xu, Z. Sha, Tao Tan, Wentao Liu, Yifu Chen, Zhanying Li, Xipeng Pan, Rongcai Jiang, Huihua Yang","doi":"10.1007/s40846-024-00892-6","DOIUrl":"https://doi.org/10.1007/s40846-024-00892-6","url":null,"abstract":"","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928030","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}
Pub Date : 2024-08-07DOI: 10.1007/s40846-024-00887-3
Kun Yang, Qiang Li, Xiaowei Zhou, Chiao-Yin Wang, Po-Hsiang Tsui
Purpose
High-intensity focused ultrasound (HIFU) is a promising non-invasive technique for thermal ablation of tumors. Positioning the focal point of the HIFU accurately prior to the procedure is crucial to the success of the treatment. A change in backscattered energy (CBE) in ultrasound images has been shown to allow visualization of thermal information and can be used to locate the focal spot of HIFU prior to ablation. In CBE imaging, however, tailing artifacts may exist below the focal point of HIFU to hinder the identification of the HIFU focal spot.
Methods
This study proposed ultrasound delta CBE (DCBE) imaging that reduces CBE artifacts by local energy subtraction between measured and the reference envelope images. Phantom experiments were performed for validation of the proposed method. A HIFU system operating at a frequency of 2.12 MHz was used to heat phantoms, which were imaged with a clinical ultrasound scanner equipped with a 3-MHz convex transducer for analysis of CBE and DCBE data.
Results
The results showed that the DCBE value increases monotonically with temperature (correlation coefficient = 0.90). Particularly, DCBE imaging can identify the HIFU focal spot, suppress tailing artifacts, and increase the contrast between the focal and artifact zones by 8 dB in comparison with conventional CBE imaging.
Conclusion
Based on this study, DCBE imaging may be an effective method of locating HIFU focal points through ultrasound backscattered energy.
{"title":"Ultrasound Delta CBE Imaging: A New Approach Based on Local Energy Subtraction to Localization of the HIFU Focal Spot Using Changes in Backscattered Energy","authors":"Kun Yang, Qiang Li, Xiaowei Zhou, Chiao-Yin Wang, Po-Hsiang Tsui","doi":"10.1007/s40846-024-00887-3","DOIUrl":"https://doi.org/10.1007/s40846-024-00887-3","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>High-intensity focused ultrasound (HIFU) is a promising non-invasive technique for thermal ablation of tumors. Positioning the focal point of the HIFU accurately prior to the procedure is crucial to the success of the treatment. A change in backscattered energy (CBE) in ultrasound images has been shown to allow visualization of thermal information and can be used to locate the focal spot of HIFU prior to ablation. In CBE imaging, however, tailing artifacts may exist below the focal point of HIFU to hinder the identification of the HIFU focal spot.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This study proposed ultrasound delta CBE (DCBE) imaging that reduces CBE artifacts by local energy subtraction between measured and the reference envelope images. Phantom experiments were performed for validation of the proposed method. A HIFU system operating at a frequency of 2.12 MHz was used to heat phantoms, which were imaged with a clinical ultrasound scanner equipped with a 3-MHz convex transducer for analysis of CBE and DCBE data.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The results showed that the DCBE value increases monotonically with temperature (correlation coefficient = 0.90). Particularly, DCBE imaging can identify the HIFU focal spot, suppress tailing artifacts, and increase the contrast between the focal and artifact zones by 8 dB in comparison with conventional CBE imaging.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Based on this study, DCBE imaging may be an effective method of locating HIFU focal points through ultrasound backscattered energy.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935015","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}
Pub Date : 2024-08-07DOI: 10.1007/s40846-024-00884-6
Hye Ryun Kim, Gahee Ahn, Helen Hong, Bong-Seog Kim
Purpose
Accurate predictions of postoperative recurrence are essential for determining appropriate follow-up treatments after surgery, as patients with non-small-cell lung cancer (NSCLC) at the same clinical stage have different recurrence incidences. However, simple convolutional neural network (CNN)-based methods are limited when presented with tumors of various sizes. This study aims to predict two-year recurrence-free survival precisely in patients with tumors of various sizes on preoperative CT images using what is termed a Tumor-Centric Attention Network (TCA-Net).
Methods
The proposed network features dual branches, each with an identical architecture but distinct weights to extract diverse features from CT images and tumor masks simultaneously. The tumor-centric attention module integrates two disparate feature maps at each level to amplify the characteristics of the tumor. All feature maps are concatenated with the finest resolution, enabling the extraction and integration of comprehensive multi-scale features for the complex tumor environment.
Results
TCA-Net showed an accuracy of 75%, balanced accuracy of 75.05%, specificity of 76.16% and an AUC value of 0.78. These results represent more balanced accuracies by 4.76% and 2.58% compared to ResNet-18 with CT images and dual ResNet-18s with CT images and tumor masks, respectively. Specifically, TCA-Net demonstrated a substantial improvement in the small-sized tumor group, achieving a balanced accuracy of 81.32%, sensitivity of 85.71%, and specificity of 76.92%.
Conclusion
TCA-Net improved the prediction performance of two-year recurrence-free survival on average across tumors of all sizes, with significant improvements, especially for small-sized tumors.
{"title":"Enhancing Two-Year Recurrence-Free Survival Prediction in Non-Small Cell Lung Cancer (NSCLC) Patients Using Tumor-Centric Attention Network (TCA-Net)","authors":"Hye Ryun Kim, Gahee Ahn, Helen Hong, Bong-Seog Kim","doi":"10.1007/s40846-024-00884-6","DOIUrl":"https://doi.org/10.1007/s40846-024-00884-6","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Accurate predictions of postoperative recurrence are essential for determining appropriate follow-up treatments after surgery, as patients with non-small-cell lung cancer (NSCLC) at the same clinical stage have different recurrence incidences. However, simple convolutional neural network (CNN)-based methods are limited when presented with tumors of various sizes. This study aims to predict two-year recurrence-free survival precisely in patients with tumors of various sizes on preoperative CT images using what is termed a Tumor-Centric Attention Network (TCA-Net).</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The proposed network features dual branches, each with an identical architecture but distinct weights to extract diverse features from CT images and tumor masks simultaneously. The tumor-centric attention module integrates two disparate feature maps at each level to amplify the characteristics of the tumor. All feature maps are concatenated with the finest resolution, enabling the extraction and integration of comprehensive multi-scale features for the complex tumor environment.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>TCA-Net showed an accuracy of 75%, balanced accuracy of 75.05%, specificity of 76.16% and an AUC value of 0.78. These results represent more balanced accuracies by 4.76% and 2.58% compared to ResNet-18 with CT images and dual ResNet-18s with CT images and tumor masks, respectively. Specifically, TCA-Net demonstrated a substantial improvement in the small-sized tumor group, achieving a balanced accuracy of 81.32%, sensitivity of 85.71%, and specificity of 76.92%.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>TCA-Net improved the prediction performance of two-year recurrence-free survival on average across tumors of all sizes, with significant improvements, especially for small-sized tumors.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935017","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}
Pub Date : 2024-08-06DOI: 10.1007/s40846-024-00893-5
Armia Ahmadi-Hadad, Egle De Rosa, Luigi Di Serafino, Giovanni Esposito
Purpose
Cardiac amyloidosis (CA) is a highly underdiagnosed disease characterized by the accumulation of misfolded amyloid protein fragments in the heart, resulting in reduced heart functionality and myocardial stiffness. Artificial intelligence (AI) has garnered considerable interest as a potential tool for diagnosing cardiovascular diseases, including CA. This systematic review concentrates on the application of AI in the diagnosis of CA.
Methods
A comprehensive systematic search was performed on the databases of PubMed, Embase, and Medline, to identify relevant studies. The screening process was conducted in two stages, using predetermined inclusion and exclusion criteria, and was carried out in a blinded manner. In cases where discrepancies arose, the reviewers discussed and resolved the issue through consensus.
Results
Following the screening process, a total of 10 studies were deemed eligible for inclusion in this review. These investigations evaluated the potential utility of AI models that analyzed routine laboratory data, medical records, ECG, transthoracic echocardiography, CMR, and WBS in the diagnosis of CA.
Conclusion
AI models have demonstrated utility as a diagnostic tool for CA, with comparable or in one case superior efficacy to that of expert cardiologists.
目的心脏淀粉样变性（CA）是一种诊断率极低的疾病，其特征是折叠错误的淀粉样蛋白片段在心脏中堆积，导致心脏功能减退和心肌僵硬。人工智能（AI）作为诊断包括淀粉样变性在内的心血管疾病的潜在工具，已经引起了人们的极大兴趣。本系统性综述集中探讨了人工智能在诊断 CA 中的应用。筛选过程分为两个阶段，采用预先确定的纳入和排除标准，并以盲法进行。结果经过筛选，共有 10 项研究被认为符合纳入本综述的条件。这些研究评估了分析常规实验室数据、病历、心电图、经胸超声心动图、CMR 和 WBS 的人工智能模型在 CA 诊断中的潜在作用。
{"title":"Artificial Intelligence as a Tool for Diagnosis of Cardiac Amyloidosis: A Systematic Review","authors":"Armia Ahmadi-Hadad, Egle De Rosa, Luigi Di Serafino, Giovanni Esposito","doi":"10.1007/s40846-024-00893-5","DOIUrl":"https://doi.org/10.1007/s40846-024-00893-5","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Cardiac amyloidosis (CA) is a highly underdiagnosed disease characterized by the accumulation of misfolded amyloid protein fragments in the heart, resulting in reduced heart functionality and myocardial stiffness. Artificial intelligence (AI) has garnered considerable interest as a potential tool for diagnosing cardiovascular diseases, including CA. This systematic review concentrates on the application of AI in the diagnosis of CA.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>A comprehensive systematic search was performed on the databases of PubMed, Embase, and Medline, to identify relevant studies. The screening process was conducted in two stages, using predetermined inclusion and exclusion criteria, and was carried out in a blinded manner. In cases where discrepancies arose, the reviewers discussed and resolved the issue through consensus.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Following the screening process, a total of 10 studies were deemed eligible for inclusion in this review. These investigations evaluated the potential utility of AI models that analyzed routine laboratory data, medical records, ECG, transthoracic echocardiography, CMR, and WBS in the diagnosis of CA.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>AI models have demonstrated utility as a diagnostic tool for CA, with comparable or in one case superior efficacy to that of expert cardiologists.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935016","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}
Pub Date : 2024-08-05DOI: 10.1007/s40846-024-00889-1
Tsung-Shine Ko, Chen-An Deng, Jiann Shieh, Hung Ji Huang, Yung-Sheng Lin, Yang-Wei Lin, Yi-Chun Du
Purpose
Surface-enhanced Raman scattering (SERS) is a technique for trace analysis detection based on the interaction of light with matter and between materials. In the past development of SERS, precious metals were primarily chosen as substrates due to their high electromagnetic effect, which leads to significantly enhanced SERS signals. However, the effect of using only precious metals is limited. Therefore, this study utilizes the characteristic micro-nano V-shaped pits that appear on the surface of c-plane GaN after wet etching. By depositing a gold film of various thicknesses, we aim to increase the contact area with the target molecule Rhodamine 6G (R6G), thereby further enhancing the sensitivity of SERS detection.
Methods
After fabricating pitted c-plane GaN using chemical etching techniques, we analyzed the sample surface with a scanning electron microscope and assessed the impact of different gold film thicknesses on the SERS intensity of R6G using Raman spectroscopy. The comprehensive biomedical detection effectiveness was also evaluated using contact angle measurement, and fluorescence microscopy.
Results
For the target molecule R6G, after depositing a 25 nm gold film, the enhancement factor of the substrate for detection reached 2.21×108, and the limit of detection was achieved at a concentration of 10− 10 M.
Conclusion
This study confirms the feasibility of using wet etching techniques on hexagonal materials like GaN for SERS applications. The GaN substrate with V-shaped pits provides an increased surface area, effectively enhancing SERS signal strength. This offers different choices and perspectives for SERS substrate selection in the detection of various target molecules.
目的表面增强拉曼散射（SERS）是一种基于光与物质以及材料之间相互作用的痕量分析检测技术。在过去的 SERS 发展中，主要选择贵金属作为基底，因为贵金属具有高电磁效应，可显著增强 SERS 信号。然而，仅使用贵金属的效果有限。因此，本研究利用了湿法蚀刻后在 c 平面氮化镓表面出现的特征性微纳 V 形坑。通过沉积不同厚度的金膜，我们旨在增加与目标分子罗丹明 6G（R6G）的接触面积，从而进一步提高 SERS 检测的灵敏度。方法利用化学蚀刻技术制作出有凹坑的 c-plane GaN 后，我们用扫描电子显微镜分析了样品表面，并用拉曼光谱评估了不同厚度的金膜对 R6G SERS 强度的影响。结果对于目标分子 R6G，在沉积 25 nm 金膜后，基底的检测增强因子达到 2.21×108，在浓度为 10- 10 M 时达到检测极限。带有 V 形凹坑的 GaN 衬底增大了表面积，有效增强了 SERS 信号强度。这为检测各种目标分子时选择 SERS 基底提供了不同的选择和前景。
{"title":"Novel GaN-Based Substrates with Gold Nanostructures for Ultra-Sensitive SERS Analysis: Micro-Nano Pit Morphology for Enhanced Molecular Detection","authors":"Tsung-Shine Ko, Chen-An Deng, Jiann Shieh, Hung Ji Huang, Yung-Sheng Lin, Yang-Wei Lin, Yi-Chun Du","doi":"10.1007/s40846-024-00889-1","DOIUrl":"https://doi.org/10.1007/s40846-024-00889-1","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Surface-enhanced Raman scattering (SERS) is a technique for trace analysis detection based on the interaction of light with matter and between materials. In the past development of SERS, precious metals were primarily chosen as substrates due to their high electromagnetic effect, which leads to significantly enhanced SERS signals. However, the effect of using only precious metals is limited. Therefore, this study utilizes the characteristic micro-nano V-shaped pits that appear on the surface of c-plane GaN after wet etching. By depositing a gold film of various thicknesses, we aim to increase the contact area with the target molecule Rhodamine 6G (R6G), thereby further enhancing the sensitivity of SERS detection.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>After fabricating pitted c-plane GaN using chemical etching techniques, we analyzed the sample surface with a scanning electron microscope and assessed the impact of different gold film thicknesses on the SERS intensity of R6G using Raman spectroscopy. The comprehensive biomedical detection effectiveness was also evaluated using contact angle measurement, and fluorescence microscopy.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>For the target molecule R6G, after depositing a 25 nm gold film, the enhancement factor of the substrate for detection reached 2.21×10<sup>8</sup>, and the limit of detection was achieved at a concentration of 10<sup>− 10</sup> M.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>This study confirms the feasibility of using wet etching techniques on hexagonal materials like GaN for SERS applications. The GaN substrate with V-shaped pits provides an increased surface area, effectively enhancing SERS signal strength. This offers different choices and perspectives for SERS substrate selection in the detection of various target molecules.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935232","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}
Pub Date : 2024-08-01DOI: 10.1007/s40846-024-00885-5
Chen-I Kao, Ben-Yi Liau, Fang-Chuan Kuo
Purpose
Objective motor and gait assessment is crucial for assessing fall risk and predicting intervention efficacy. This study compares the postural control and gait complexity in adults with and without vertebral compression fracture (VCF) during the timed up-and-go (TUG) test.
Methods
The groups of eligible older adults were divided into VCF (n = 21) and Control (without VCF; n = 43). The Biodex Balance System was used for postural stability and motor control tests. A TUG test was conducted, during which an inertial motion system was used to record joint kinematics and center of mass (CoM) trajectories. The gait complexity was assessed using multiscale entropy (MSE) analysis of pelvic acceleration.
Results
The VCF group had poor postural stability and longer times in the motor control test than the control group. During the sit-to-stand phase of the TUG test, the VCF group exhibited more significant mediolateral CoM displacement and less anteroposterior displacement than the control group. The VCF group had more significant vertical CoM displacement, lower acceleration, and lower ranges of motion in the cervical, thoracic, lumbar, and hip joints and longer stance phases than the control group while performing the TUG test. Furthermore, the VCF group had a higher complexity index of gait, indicating lower adaptability than the control group during walking.
Conclusion
Patients with VCF exhibited lower postural stability, potentially increasing their risk of falls. The patients adopted various less stable body configurations during the TUG test. Gait quality characteristics measured through MSE analysis may help evaluate the walking adaptability of individuals at risk of falls.
{"title":"Measures of Gait Complexity during the Timed Up-and-Go Test in Older Adults with Vertebral Compression Fracture","authors":"Chen-I Kao, Ben-Yi Liau, Fang-Chuan Kuo","doi":"10.1007/s40846-024-00885-5","DOIUrl":"https://doi.org/10.1007/s40846-024-00885-5","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Objective motor and gait assessment is crucial for assessing fall risk and predicting intervention efficacy. This study compares the postural control and gait complexity in adults with and without vertebral compression fracture (VCF) during the timed up-and-go (TUG) test.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The groups of eligible older adults were divided into VCF (<i>n</i> = 21) and Control (without VCF; <i>n</i> = 43). The Biodex Balance System was used for postural stability and motor control tests. A TUG test was conducted, during which an inertial motion system was used to record joint kinematics and center of mass (CoM) trajectories. The gait complexity was assessed using multiscale entropy (MSE) analysis of pelvic acceleration.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The VCF group had poor postural stability and longer times in the motor control test than the control group. During the sit-to-stand phase of the TUG test, the VCF group exhibited more significant mediolateral CoM displacement and less anteroposterior displacement than the control group. The VCF group had more significant vertical CoM displacement, lower acceleration, and lower ranges of motion in the cervical, thoracic, lumbar, and hip joints and longer stance phases than the control group while performing the TUG test. Furthermore, the VCF group had a higher complexity index of gait, indicating lower adaptability than the control group during walking.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Patients with VCF exhibited lower postural stability, potentially increasing their risk of falls. The patients adopted various less stable body configurations during the TUG test. Gait quality characteristics measured through MSE analysis may help evaluate the walking adaptability of individuals at risk of falls.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866218","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}
Mild cognitive impairment (MCI) and Alzheimer’s disease (AD) are known to cause geometrical changes in the integrity of the fornix, which plays a crucial role in memory formation and retrieval. The objective of this study is to analyse structural variations in the fornix region using structural magnetic resonance (sMR) images and geometrical features.
Methods
Initially, the fornix region of the brain is segmented from the sMR images of normal cognitive (NC), MCI and AD using the FreeSurfer software package. Further, geometrical features such as volume, equivalent diameter, extent, major axis length, and solidity are extracted to investigate the changes in the structure of the fornix in MCI and AD conditions.
Results
The segmentation results show that FreeSurfer software is able to delineate the irregular boundaries of the fornix region accurately. The extent, major axis length, and solidity features are found to be statistically significant (p < 0.001) in discriminating NC, MCI and AD. It indicates that the considered features can capture the geometrical variation in the fornix structure.
Conclusion
The reported approach can facilitate the early diagnosis of the disease, as the distinction of AD in the preclinical stage is complex and clinically significant.
目的众所周知，轻度认知障碍（MCI）和阿尔茨海默病（AD）会导致穹窿的完整性发生几何变化，而穹窿在记忆的形成和检索中起着至关重要的作用。本研究的目的是利用结构磁共振（sMR）图像和几何特征来分析穹窿区的结构变化。方法首先，使用 FreeSurfer 软件包从正常认知（NC）、MCI 和 AD 的 sMR 图像中分割出大脑的穹窿区。结果分割结果表明，FreeSurfer 软件能准确划分出穹窿区域的不规则边界。在区分 NC、MCI 和 AD 时，发现范围、主轴长度和稳固性特征具有统计学意义（p < 0.001）。结论：由于临床前阶段的 AD 区分复杂且具有临床意义，因此所报告的方法有助于疾病的早期诊断。
{"title":"Assessment of Structural Variations in Fornix of MCI and AD Using MR Images and Geometrical Features","authors":"Ahsan Ali, Jac Fredo Agastinose Ronickom, Ramakrishnan Swaminathan","doi":"10.1007/s40846-024-00883-7","DOIUrl":"https://doi.org/10.1007/s40846-024-00883-7","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Mild cognitive impairment (MCI) and Alzheimer’s disease (AD) are known to cause geometrical changes in the integrity of the fornix, which plays a crucial role in memory formation and retrieval. The objective of this study is to analyse structural variations in the fornix region using structural magnetic resonance (sMR) images and geometrical features.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Initially, the fornix region of the brain is segmented from the sMR images of normal cognitive (NC), MCI and AD using the FreeSurfer software package. Further, geometrical features such as volume, equivalent diameter, extent, major axis length, and solidity are extracted to investigate the changes in the structure of the fornix in MCI and AD conditions.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The segmentation results show that FreeSurfer software is able to delineate the irregular boundaries of the fornix region accurately. The extent, major axis length, and solidity features are found to be statistically significant (<i>p</i> < 0.001) in discriminating NC, MCI and AD. It indicates that the considered features can capture the geometrical variation in the fornix structure.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The reported approach can facilitate the early diagnosis of the disease, as the distinction of AD in the preclinical stage is complex and clinically significant.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866219","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}
Pub Date : 2024-07-29DOI: 10.1007/s40846-024-00886-4
Huizhong Ji, Zhili Zhang, Peng Xue, Meirong Ren, Enqing Dong
Purpose
Image registration is a critical component in medical image analysis applications. Optimization algorithms for energy functions play a crucial role in registration. Most registration methods improve the performance by modifying the energy function and optimizing it directly, neglecting the impact of the optimization algorithm. This paper is to investigate how to efficiently design an attention allocation strategy and improve the convergence of the optimization algorithm.
Methods
This paper introduces a novel image registration method that leverages the distributed alternating direction method of multipliers to perform optimization, named DADMMreg. Compared to the optimization algorithm using the alternating direction method of multipliers (ADMM), the optimization algorithm used in DADMMreg achieves better convergence by altering the optimization order of the similarity and regularization terms within the energy function. To overcome the limitations of intensity-based or structural-based similarity metrics, a modified structural similarity measure (SSIM) is proposed that takes into account both intensity and structural information. Considering that homogeneous smoothing prior at the sliding surface leads to inaccurate registration, a novel vector-modulus-based regularization metric is proposed to avoid physically implausible displacement fields.
Results
Experimental results on 4D-CT image dataset and COPD image dataset demonstrate the satisfactory registration performance of DADMMreg, with an average target registration error (TRE) of 0.9105 mm and 0.9201 mm, respectively. Meanwhile, the experimental results show that the DADMMreg method exhibits better convergence performance than other registration methods.
Conclusion
Compared to classical methods, the attention allocation strategy of DADMMreg enables faster convergence with comparable registration accuracy.
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This systematic review and meta-analysis was conducted to evaluate the usefulness of deep learning (DL) models for aorta segmentation in computed tomography (CT) images.
Methods
Adhering to 2020 PRISMA guidelines, we systematically searched PubMed, Embase, and Web of Science for studies published up to March 13, 2024, that used DL models for aorta segmentation in adults’ chest CT images. We excluded studies that did not use DL models, involved nonhuman subjects or aortic diseases (aneurysms and dissections), or lacked essential data for meta-analysis. Segmentation performance was evaluated primarily in terms of Dice scores. Subgroup analyses were performed to identify variations related to geographical location and methodology.
Results
Our review of 16 studies indicated that DL models achieve high segmentation accuracy, with a pooled Dice score of 96%. We further noted geographical variations in model performance but no significant publication bias, according to the Egger test.
Conclusion
DL models facilitate aorta segmentation in CT images, and they can therefore guide accurate, efficient, and standardized diagnosis and treatment planning for cardiovascular diseases. Future studies should address the current challenges to enhance model generalizability and evaluate clinical benefits and thus expand the application of DL models in clinical practice.
{"title":"Deep Learning Models for Aorta Segmentation in Computed Tomography Images: A Systematic Review And Meta-Analysis","authors":"Ting-Wei Wang, Yun-Hsuan Tzeng, Jia-Sheng Hong, Ho-Ren Liu, Kuan-Ting Wu, Hao-Neng Fu, Yung-Tsai Lee, Wei-Hsian Yin, Yu-Te Wu","doi":"10.1007/s40846-024-00881-9","DOIUrl":"https://doi.org/10.1007/s40846-024-00881-9","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>This systematic review and meta-analysis was conducted to evaluate the usefulness of deep learning (DL) models for aorta segmentation in computed tomography (CT) images.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Adhering to 2020 PRISMA guidelines, we systematically searched PubMed, Embase, and Web of Science for studies published up to March 13, 2024, that used DL models for aorta segmentation in adults’ chest CT images. We excluded studies that did not use DL models, involved nonhuman subjects or aortic diseases (aneurysms and dissections), or lacked essential data for meta-analysis. Segmentation performance was evaluated primarily in terms of Dice scores. Subgroup analyses were performed to identify variations related to geographical location and methodology.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Our review of 16 studies indicated that DL models achieve high segmentation accuracy, with a pooled Dice score of 96%. We further noted geographical variations in model performance but no significant publication bias, according to the Egger test.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>DL models facilitate aorta segmentation in CT images, and they can therefore guide accurate, efficient, and standardized diagnosis and treatment planning for cardiovascular diseases. Future studies should address the current challenges to enhance model generalizability and evaluate clinical benefits and thus expand the application of DL models in clinical practice.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141770089","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}
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