BME frontiers最新文献

{"title":"Blood-Brain Barrier Opening by Individualized Closed-Loop Feedback Control of Focused Ultrasound.","authors":"Chih-Yen Chien, Yaoheng Yang, Yan Gong, Yimei Yue, Hong Chen","doi":"10.34133/2022/9867230","DOIUrl":"10.34133/2022/9867230","url":null,"abstract":"<p><p><i>Objective and Impact Statement</i>. To develop an approach for individualized closed-loop feedback control of microbubble cavitation to achieve safe and effective focused ultrasound in combination with microbubble-induced blood-brain barrier opening (FUS-BBBO). <i>Introduction</i>. FUS-BBBO is a promising strategy for noninvasive and localized brain drug delivery with a growing number of clinical studies currently ongoing. Real-time cavitation monitoring and feedback control are critical to achieving safe and effective FUS-BBBO. However, feedback control algorithms used in the past were either open-loop or without consideration of baseline cavitation level difference among subjects. <i>Methods</i>. This study performed feedback-controlled FUS-BBBO by defining the target cavitation level based on the baseline stable cavitation level of an individual subject with \"dummy\" FUS sonication. The dummy FUS sonication applied FUS with a low acoustic pressure for a short duration in the presence of microbubbles to define the baseline stable cavitation level that took into consideration of individual differences in the detected cavitation emissions. FUS-BBBO was then achieved through two sonication phases: ramping-up phase to reach the target cavitation level and maintaining phase to control the stable cavitation level at the target cavitation level. <i>Results</i>. Evaluations performed in wild-type mice demonstrated that this approach achieved effective and safe trans-BBB delivery of a model drug. The drug delivery efficiency increased as the target cavitation level increased from 0.5 dB to 2 dB without causing vascular damage. Increasing the target cavitation level to 3 dB and 4 dB increased the probability of tissue damage. <i>Conclusions</i>. Safe and effective brain drug delivery was achieved using the individualized closed-loop feedback-controlled FUS-BBBO.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9867230"},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma.","authors":"Jifei Wang,&nbsp;Dasheng Wu,&nbsp;Meili Sun,&nbsp;Zhenpeng Peng,&nbsp;Yingyu Lin,&nbsp;Hongxin Lin,&nbsp;Jiazhao Chen,&nbsp;Tingyu Long,&nbsp;Zi-Ping Li,&nbsp;Chuanmiao Xie,&nbsp;Bingsheng Huang,&nbsp;Shi-Ting Feng","doi":"10.34133/2022/9793716","DOIUrl":"https://doi.org/10.34133/2022/9793716","url":null,"abstract":"<p><p><i>Objective and Impact Statement</i>. This study developed and validated a deep semantic segmentation feature-based radiomics (DSFR) model based on preoperative contrast-enhanced computed tomography (CECT) combined with clinical information to predict early recurrence (ER) of single hepatocellular carcinoma (HCC) after curative resection. ER prediction is of great significance to the therapeutic decision-making and surveillance strategy of HCC. <i>Introduction</i>. ER prediction is important for HCC. However, it cannot currently be adequately determined. <i>Methods</i>. Totally, 208 patients with single HCC after curative resection were retrospectively recruited into a model-development cohort (<math><mi>n</mi><mo>=</mo><mn>180</mn></math>) and an independent validation cohort (<math><mi>n</mi><mo>=</mo><mn>28</mn></math>). DSFR models based on different CT phases were developed. The optimal DSFR model was incorporated with clinical information to establish a DSFR-C model. An integrated nomogram based on the Cox regression was established. The DSFR signature was used to stratify high- and low-risk ER groups. <i>Results</i>. A portal phase-based DSFR model was selected as the optimal model (area under receiver operating characteristic curve (AUC): development cohort, 0.740; validation cohort, 0.717). The DSFR-C model achieved AUCs of 0.782 and 0.744 in the development and validation cohorts, respectively. In the development and validation cohorts, the integrated nomogram achieved C-index of 0.748 and 0.741 and time-dependent AUCs of 0.823 and 0.822, respectively, for recurrence-free survival (RFS) prediction. The RFS difference between the risk groups was statistically significant (<math><mi>P</mi><mo><</mo><mn>0.0001</mn></math> and <math><mi>P</mi><mo>=</mo><mn>0.045</mn></math> in the development and validation cohorts, respectively). <i>Conclusion</i>. CECT-based DSFR can predict ER in single HCC after curative resection, and its combination with clinical information further improved the performance for ER prediction.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9793716"},"PeriodicalIF":0.0,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders.","authors":"Wei Xiong,&nbsp;Neil Yeung,&nbsp;Shubo Wang,&nbsp;Haofu Liao,&nbsp;Liyun Wang,&nbsp;Jiebo Luo","doi":"10.34133/2022/9763284","DOIUrl":"10.34133/2022/9763284","url":null,"abstract":"<p><p><i>Objective and Impact Statement</i>. We adopt a deep learning model for bone osteolysis prediction on computed tomography (CT) images of murine breast cancer bone metastases. Given the bone CT scans at previous time steps, the model incorporates the bone-cancer interactions learned from the sequential images and generates future CT images. Its ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone metastasis. <i>Introduction</i>. Breast cancer often metastasizes to bone, causes osteolytic lesions, and results in skeletal-related events (SREs) including severe pain and even fatal fractures. Although current imaging techniques can detect macroscopic bone lesions, predicting the occurrence and progression of bone lesions remains a challenge. <i>Methods</i>. We adopt a temporal variational autoencoder (T-VAE) model that utilizes a combination of variational autoencoders and long short-term memory networks to predict bone lesion emergence on our micro-CT dataset containing sequential images of murine tibiae. Given the CT scans of murine tibiae at early weeks, our model can learn the distribution of their future states from data. <i>Results</i>. We test our model against other deep learning-based prediction models on the bone lesion progression prediction task. Our model produces much more accurate predictions than existing models under various evaluation metrics. <i>Conclusion</i>. We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent SREs in breast cancer patients.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9763284"},"PeriodicalIF":0.0,"publicationDate":"2022-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning.","authors":"Hailing Liu,&nbsp;Yu Zhao,&nbsp;Fan Yang,&nbsp;Xiaoying Lou,&nbsp;Feng Wu,&nbsp;Hang Li,&nbsp;Xiaohan Xing,&nbsp;Tingying Peng,&nbsp;Bjoern Menze,&nbsp;Junzhou Huang,&nbsp;Shujun Zhang,&nbsp;Anjia Han,&nbsp;Jianhua Yao,&nbsp;Xinjuan Fan","doi":"10.34133/2022/9860179","DOIUrl":"https://doi.org/10.34133/2022/9860179","url":null,"abstract":"<p><p><i>Objective</i>. To develop an artificial intelligence method predicting lymph node metastasis (LNM) for patients with colorectal cancer (CRC). <i>Impact Statement</i>. A novel interpretable multimodal AI-based method to predict LNM for CRC patients by integrating information of pathological images and serum tumor-specific biomarkers. <i>Introduction</i>. Preoperative diagnosis of LNM is essential in treatment planning for CRC patients. Existing radiology imaging and genomic tests approaches are either unreliable or too costly. <i>Methods</i>. A total of 1338 patients were recruited, where 1128 patients from one centre were included as the discovery cohort and 210 patients from other two centres were involved as the external validation cohort. We developed a Multimodal Multiple Instance Learning (MMIL) model to learn latent features from pathological images and then jointly integrated the clinical biomarker features for predicting LNM status. The heatmaps of the obtained MMIL model were generated for model interpretation. <i>Results</i>. The MMIL model outperformed preoperative radiology-imaging diagnosis and yielded high area under the curve (AUCs) of 0.926, 0.878, 0.809, and 0.857 for patients with stage T1, T2, T3, and T4 CRC, on the discovery cohort. On the external cohort, it obtained AUCs of 0.855, 0.832, 0.691, and 0.792, respectively (T1-T4), which indicates its prediction accuracy and potential adaptability among multiple centres. <i>Conclusion</i>. The MMIL model showed the potential in the early diagnosis of LNM by referring to pathological images and tumor-specific biomarkers, which is easily accessed in different institutes. We revealed the histomorphologic features determining the LNM prediction indicating the model ability to learn informative latent features.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9860179"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution.","authors":"Peiting You, Xiang Li, Fan Zhang, Quanzheng Li","doi":"10.34133/2022/9814824","DOIUrl":"10.34133/2022/9814824","url":null,"abstract":"<p><p><i>Objective</i>. Objective of this work is the development and evaluation of a cortical parcellation framework based on tractography-derived brain structural connectivity. <i>Impact Statement</i>. The proposed framework utilizes novel spatial-graph representation learning methods for solving the task of cortical parcellation, an important medical image analysis and neuroscientific problem. <i>Introduction</i>. The concept of \"connectional fingerprint\" has motivated many investigations on the connectivity-based cortical parcellation, especially with the technical advancement of diffusion imaging. Previous studies on multiple brain regions have been conducted with promising results. However, performance and applicability of these models are limited by the relatively simple computational scheme and the lack of effective representation of brain imaging data. <i>Methods</i>. We propose the Spatial-graph Convolution Parcellation (SGCP) framework, a two-stage deep learning-based modeling for the graph representation brain imaging. In the first stage, SGCP learns an effective embedding of the input data through a self-supervised contrastive learning scheme with the backbone encoder of a spatial-graph convolution network. In the second stage, SGCP learns a supervised classifier to perform voxel-wise classification for parcellating the desired brain region. <i>Results</i>. SGCP is evaluated on the parcellation task for 5 brain regions in a 15-subject DWI dataset. Performance comparisons between SGCP, traditional parcellation methods, and other deep learning-based methods show that SGCP can achieve superior performance in all the cases. <i>Conclusion</i>. Consistent good performance of the proposed SGCP framework indicates its potential to be used as a general solution for investigating the regional/subregional composition of human brain based on one or more connectivity measurements.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9814824"},"PeriodicalIF":5.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Performance Magnetic-core Coils for Targeted Rodent Brain Stimulations.","authors":"Hedyeh Bagherzadeh,&nbsp;Qinglei Meng,&nbsp;Hanbing Lu,&nbsp;Elliott Hong,&nbsp;Yihong Yang,&nbsp;Fow-Sen Choa","doi":"10.34133/2022/9854846","DOIUrl":"https://doi.org/10.34133/2022/9854846","url":null,"abstract":"<p><p><i>Objective and Impact Statement</i>. There is a need to develop rodent coils capable of targeted brain stimulation for treating neuropsychiatric disorders and understanding brain mechanisms. We describe a novel rodent coil design to improve the focality for targeted stimulations in small rodent brains. <i>Introduction</i>. Transcranial magnetic stimulation (TMS) is becoming increasingly important for treating neuropsychiatric disorders and understanding brain mechanisms. Preclinical studies permit invasive manipulations and are essential for the mechanistic understanding of TMS effects and explorations of therapeutic outcomes in disease models. However, existing TMS tools lack focality for targeted stimulations. Notably, there has been limited fundamental research on developing coils capable of focal stimulation at deep brain regions on small animals like rodents. <i>Methods</i>. In this study, ferromagnetic cores are added to a novel angle-tuned coil design to enhance the coil performance regarding penetration depth and focality. Numerical simulations and experimental electric field measurements were conducted to optimize the coil design. <i>Results</i>. The proposed coil system demonstrated a significantly smaller stimulation spot size and enhanced electric field decay rate in comparison to existing coils. Adding the ferromagnetic core reduces the energy requirements up to 60% for rodent brain stimulation. The simulated results are validated with experimental measurements and demonstration of suprathreshold rodent limb excitation through targeted motor cortex activation. <i>Conclusion</i>. The newly developed coils are suitable tools for focal stimulations of the rodent brain due to their smaller stimulation spot size and improved electric field decay rate.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9854846"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weakly- and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts.","authors":"Alex Ling Yu Hung,&nbsp;Edward Chen,&nbsp;John Galeotti","doi":"10.34133/2022/9837076","DOIUrl":"10.34133/2022/9837076","url":null,"abstract":"Objective and Impact Statement. We propose a weakly- and semisupervised, probabilistic needle-and-reverberation-artifact segmentation algorithm to separate the desired tissue-based pixel values from the superimposed artifacts. Our method models the intensity decay of artifact intensities and is designed to minimize the human labeling error. Introduction. Ultrasound image quality has continually been improving. However, when needles or other metallic objects are operating inside the tissue, the resulting reverberation artifacts can severely corrupt the surrounding image quality. Such effects are challenging for existing computer vision algorithms for medical image analysis. Needle reverberation artifacts can be hard to identify at times and affect various pixel values to different degrees. The boundaries of such artifacts are ambiguous, leading to disagreement among human experts labeling the artifacts. Methods. Our learning-based framework consists of three parts. The first part is a probabilistic segmentation network to generate the soft labels based on the human labels. These soft labels are input into the second part which is the transform function, where the training labels for the third part are generated. The third part outputs the final masks which quantifies the reverberation artifacts. Results. We demonstrate the applicability of the approach and compare it against other segmentation algorithms. Our method is capable of both differentiating between the reverberations from artifact-free patches and modeling the intensity fall-off in the artifacts. Conclusion. Our method matches state-of-the-art artifact segmentation performance and sets a new standard in estimating the per-pixel contributions of artifact vs underlying anatomy, especially in the immediately adjacent regions between reverberation lines. Our algorithm is also able to improve the performance of downstream image analysis algorithms.","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9837076"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Noninvasive Ultrasound Retinal Stimulation for Vision Restoration at High Spatiotemporal Resolution.","authors":"Xuejun Qian,&nbsp;Gengxi Lu,&nbsp;Biju B Thomas,&nbsp;Runze Li,&nbsp;Xiaoyang Chen,&nbsp;K Kirk Shung,&nbsp;Mark Humayun,&nbsp;Qifa Zhou","doi":"10.34133/2022/9829316","DOIUrl":"10.34133/2022/9829316","url":null,"abstract":"<p><p><i>Objective</i>. Retinal degeneration involving progressive deterioration and loss of function of photoreceptors is a major cause of permanent vision loss worldwide. Strategies to treat these incurable conditions incorporate retinal prostheses via electrically stimulating surviving retinal neurons with implanted devices in the eye, optogenetic therapy, and sonogenetic therapy. Existing challenges of these strategies include invasive manner, complex implantation surgeries, and risky gene therapy. <i>Methods and Results</i>. Here, we show that direct ultrasound stimulation on the retina can evoke neuron activities from the visual centers including the superior colliculus and the primary visual cortex (V1), in either normal-sighted or retinal degenerated blind rats <i>in vivo</i>. The neuron activities induced by the customized spherically focused 3.1 MHz ultrasound transducer have shown both good spatial resolution of 250 <i>μ</i>m and temporal resolution of 5 Hz in the rat visual centers. An additional customized 4.4 MHz helical transducer was further implemented to generate a static stimulation pattern of letter forms. <i>Conclusion</i>. Our findings demonstrate that ultrasound stimulation of the retina <i>in vivo</i> is a safe and effective approach with high spatiotemporal resolution, indicating a promising future of ultrasound stimulation as a novel and noninvasive visual prosthesis for translational applications in blind patients.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9829316"},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound-Mediated Drug Delivery: Sonoporation Mechanisms, Biophysics, and Critical Factors.","authors":"Juan Tu, Alfred C H Yu","doi":"10.34133/2022/9807347","DOIUrl":"10.34133/2022/9807347","url":null,"abstract":"<p><p>Sonoporation, or the use of ultrasound in the presence of cavitation nuclei to induce plasma membrane perforation, is well considered as an emerging physical approach to facilitate the delivery of drugs and genes to living cells. Nevertheless, this emerging drug delivery paradigm has not yet reached widespread clinical use, because the efficiency of sonoporation is often deemed to be mediocre due to the lack of detailed understanding of the pertinent scientific mechanisms. Here, we summarize the current observational evidence available on the notion of sonoporation, and we discuss the prevailing understanding of the physical and biological processes related to sonoporation. To facilitate systematic understanding, we also present how the extent of sonoporation is dependent on a multitude of factors related to acoustic excitation parameters (ultrasound frequency, pressure, cavitation dose, exposure time), microbubble parameters (size, concentration, bubble-to-cell distance, shell composition), and cellular properties (cell type, cell cycle, biochemical contents). By adopting a science-backed approach to the realization of sonoporation, ultrasound-mediated drug delivery can be more controllably achieved to viably enhance drug uptake into living cells with high sonoporation efficiency. This drug delivery approach, when coupled with concurrent advances in ultrasound imaging, has potential to become an effective therapeutic paradigm.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9807347"},"PeriodicalIF":5.0,"publicationDate":"2022-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus.","authors":"Angela Zhang,&nbsp;Amil Khan,&nbsp;Saisidharth Majeti,&nbsp;Judy Pham,&nbsp;Christopher Nguyen,&nbsp;Peter Tran,&nbsp;Vikram Iyer,&nbsp;Ashutosh Shelat,&nbsp;Jefferson Chen,&nbsp;B S Manjunath","doi":"10.34133/2022/9783128","DOIUrl":"10.34133/2022/9783128","url":null,"abstract":"<p><p><i>Objective and Impact Statement</i>. We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans. These regions are then combined with MRI information to predict NPH. To our knowledge, this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for prediction. <i>Introduction</i>. Due to their low cost and high versatility, CT scans are often used in NPH diagnosis. No well-defined and effective protocol currently exists for analysis of CT scans for NPH. Evans' index, an approximation of the ventricle to brain volume using one 2D image slice, has been proposed but is not robust. The proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting NPH. <i>Methods</i>. We propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these regions. The segmentation and network features are used to train a model for NPH prediction. <i>Results</i>. Our method outperforms the current state-of-the-art by 9 precision points and 29 recall points. Our segmentation model outperforms the current state-of-the-art in segmenting the ventricle, gray-white matter, and subarachnoid space in CT scans. <i>Conclusion</i>. Our experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process, and network properties can increase NPH prediction accuracy.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9783128"},"PeriodicalIF":0.0,"publicationDate":"2022-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}