Bioimaging (Bristol. Print)最新文献

{"title":"Comparison of Gadolinium Contrast Agent Retention in Patients Receiving Multiple Contrast-enhanced MRI Exams","authors":"R. Fisher, V. Jain, J. Glaab, Aubrey McMillan","doi":"10.5220/0008909101090115","DOIUrl":"https://doi.org/10.5220/0008909101090115","url":null,"abstract":": Gadolinium-based contrast agents have long been utilized in magnetic resonance imaging (MRI) to enhance image quality. Aside from the few reported cases of Nephrogenic Systemic Fibrosis in patients with severely compromised renal function, these contrast agents have generally been viewed as safe. However, recent studies have shown evidence of the retention of potentially toxic gadolinium well beyond the previously recognized clearing times in patients with normal renal function. This retention has been shown via persistent hyper-intense signal in certain brain regions in unenhanced MRI exams. The exact form of retained gadolinium and its long-term potential health effects remain unknown at this time. Due to concerns over retained gadolinium, our hospital switched to a more stably bound contrast agent in the spring of 2018. This study examined brain MRI images from patients with multiple contrast-enhanced exams using either the older, more unstable, linear agent, and the newer, more stable, macrocyclic agent. Signal intensities were measured in the globus pallidus and dentate nucleus; regions of the brain that have previously been shown to accumulate heavy metals such as gadolinium. Statistically significant increases in signal intensity were seen in the dentate nucleus in the linear contrast agent group, but not in the macrocyclic agent group. No significant signal increases were seen with either agent in the globus pallidus region of the brain. No correlation was seen between signal increase and the volume of contrast agent administered for either region or contrast agent.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116768950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 Diagnosis using Single-modality and Joint Fusion Deep Convolutional Neural Network Models","authors":"Sara El-Ateif, A. Idri","doi":"10.5220/0010897100003123","DOIUrl":"https://doi.org/10.5220/0010897100003123","url":null,"abstract":"COVID-19 is a recently emerged pneumonia disease with threatening complications that can be avoided by early diagnosis. Deep learning (DL) multimodality fusion is rapidly becoming state of the art, leading to enhanced performance in various medical applications such as cognitive impairment diseases and lung cancer. In this paper, for COVID-19 detection, seven deep learning models (VGG19, DenseNet121, InceptionV3, InceptionResNetV2, Xception, ResNet50V2, and MobileNetV2) using single-modality and joint fusion were empirically examined and contrasted in terms of accuracy, area under the curve, sensitivity, specificity, precision, and Fl-score with Scott-Knott Effect Size Difference statistical test and Borda Count voting method. The empirical evaluations were conducted over two datasets: COVID-19 Radiography Database and COVID-CT using 5-fold cross validation. Results showed that MobileNetV2 was the best performing and less sensitive technique on the two datasets using mono-modality with an accuracy value of 78% for Computed Tomography (CT) and 92% for Chest X-Ray (CXR) modalities. Joint fusion outperformed mono-modality DL techniques, with MobileNetV2, ResNet50V2 and InceptionResNetV2 joint fusion as the best performing for COVID-19 diagnosis with an accuracy of 99%. Therefore, we recommend the use of the joint fusion DL models MobileNetV2, ResNet50V2 and InceptionResNetV2 for the detection of COVID-19. As for monomodality, MobileNetV2 was the best in performance and less sensitive model to the two imaging modalities.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132117598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Deep Learning Uncertainty Measures in Cephalometric Landmark Localization","authors":"Dusan Drevický, O. Kodym","doi":"10.5220/0009375302130220","DOIUrl":"https://doi.org/10.5220/0009375302130220","url":null,"abstract":"Cephalometric analysis is a key step in the process of dental treatment diagnosis, planning and surgery. Localization of a set of landmark points is an important but time-consuming and subjective part of this task. Deep learning is able to automate this process but the model predictions are usually given without any uncertainty information which is necessary in medical applications. This work evaluates three uncertainty measures applicable to deep learning models on the task of cephalometric landmark localization. We compare uncertainty estimation based on final network activation with an ensemble-based and a Bayesian-based approach. We conduct two experiments with elastically distorted cephalogram images and images containing undesirable horizontal skull rotation which the models should be able to detect as unfamiliar and unsuitable for automatic evaluation. We show that all three uncertainty measures have this detection capability and are a viable option when landmark localization with uncertainty estimation is required.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134240720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"fMRI and Voxel-based Morphometry in Detection of Early Stages of Alzheimer's Disease","authors":"A. Sokolov, S. Vorobyev, A. Efimtcev, V. S. Dekan, G. Trufanov, V. Lobzin, V. Fokin","doi":"10.5220/0006109600670071","DOIUrl":"https://doi.org/10.5220/0006109600670071","url":null,"abstract":"Alzheimer’s disease (AD) is the most common form of dementia in older adults. Loss of memory is the usual first symptom and different brain regions are involved to this pathological process. The aim of the study was to investigate the organization of cortical areas responsible for visual memory and determine correlation between memory impairment and atrophy of memory specific brain regions in early stages of AD. Voxel-based MR-morphometry was used to evaluate brain atrophy and functional MRI was used to detect specific brain regions responsible to visual memory task in patients with Alzheimer's disease and in control group. FMRI was performed on Siemens Magnetom Symphony (1.5 T ) with the use of Blood Oxygenation Level Dependent technique (BOLD), based on distinctions of magnetic properties of hemoglobin. For test stimuli we used blocks of 12 not related images for \"Baseline\" and 12 images with 6 presented before for \"Active\". Stimuli were presented 3 times with reduction of repeated images to 4 and 2. For functional and morthometric data post-processing we used SPM8. Patients with Alzheimer's disease showed less activation in hippocampal formation (HF) region and parahippocampal gyrus then the control group (p<0.05). The study also showed reduced activation in posterior cingulate cortex (p<0.001). Voxelbased morphometry showed significant atrophy of grey matter in Alzheimer’s disease patients, especially of both temporal lobes (fusiform and parahippocampal gyri); frontal lobes (posterior cingulate and superior frontal gyri). The study showed correlation between memory impairment and atrophy of memory specific brain regions of frontal and medial temporal lobes. Reduced activation in hippocampal formation and parahippocampal gyri, in posterior cingulate gyrus in patients with Alzheimer's disease correlates to significant atrophy of these regions, detected by voxel-based morphometry. The use of functional MRI and voxel-based morphometry provides the way to find alterations in brain function on early stages of AD before the development of significant irreversible structural damage.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133796192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bacterial Growth and Siderophore Production in Bacteria: An Analytical Model","authors":"Gennadi Saiko","doi":"10.5220/0010342901880192","DOIUrl":"https://doi.org/10.5220/0010342901880192","url":null,"abstract":"We have analyzed the impact of quorum sensing and resource dependency on the production of critically crucial for bacteria fitness compounds (siderophores). We have built two siderophore production models (quorum sensing and resource dependency) and linked them with Monod’s growth model. As a result, siderophore accumulation is explicitly expressed through bacterial concentration N, which allows direct experimental verification. A nutrient-dependent model predicts three siderophore accumulation phases, which accompany bacterial growth: slow accumulation for [N0, Nth], fast accumulation for [Nth, K/2], and slow or no accumulation for [K/2, K). Here N0 is the initial bacterial concentration, K is the carrying capacity. A quorum-sensing model predicts two regimes of siderophore accumulation: relatively slow accumulation for [N0, Ncr] and much faster non-linear accumulation for [Ncr, K). Ncr and Nth are model parameters. Ncr has an “absolute” value. It is dependent on bacterial strain only. Nth has a “relative” value. In addition to the bacterial strain, it also depends on inoculums concentration and the initial nutrient concentration. Such as models predict entirely different behavior, experimental data may help differentiate between these mechanisms.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114708088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep-Learning-based Segmentation of Organs-at-Risk in the Head for MR-assisted Radiation Therapy Planning","authors":"L. Ruskó, M. Capala, V. Czipczer, B. Kolozsvári, B. Deák-Karancsi, R. Czabány, B. Gyalai, T. Tan, Z. Végváry, E. Borzasi, Zsófia Együd, Renáta Kószó, Viktor R. Paczona, Emese Fodor, C. Bobb, C. Cozzini, S. Kaushik, Barbara Darázs, G. Verduijn, R. Pearson, R. Maxwell, H. Mccallum, J. Tamames, K. Hideghéty, S. Petit, F. Wiesinger","doi":"10.5220/0010235000310043","DOIUrl":"https://doi.org/10.5220/0010235000310043","url":null,"abstract":"Segmentation of organs-at-risk (OAR) in MR images has several clinical applications; including radiation therapy (RT) planning. This paper presents a deep-learning-based method to segment 15 structures in the head region. The proposed method first applies 2D U-Net models to each of the three planes (axial, coronal, sagittal) to roughly segment the structure. Then, the results of the 2D models are combined into a fused prediction to localize the 3D bounding box of the structure. Finally, a 3D U-Net is applied to the volume of the bounding box to determine the precise contour of the structure. The model was trained on a public dataset and evaluated on both public and private datasets that contain T2-weighted MR scans of the head-and-neck region. For all cases the contour of each structure was defined by operators trained by expert clinical delineators. The evaluation demonstrated that various structures can be accurately and efficiently localized and segmented using the presented framework. The contours generated by the proposed method were also qualitatively evaluated. The majority (92%) of the segmented OARs was rated as clinically useful for radiation","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124681581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Particle Tracking with Neighbourhood Similarities: A New Method for Super Resolution Ultrasound Imaging","authors":"A. Mobberley, G. Papageorgiou, M. Butler, E. Kanoulas, J. Keanie, D. Good, K. Gallagher, Alan McNeil, V. Sboros, Weiping Lu","doi":"10.5220/0011613900003414","DOIUrl":"https://doi.org/10.5220/0011613900003414","url":null,"abstract":"","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122818023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosted Tree Classifier for in Vivo Identification of Early Cervical Cancer using Multispectral Digital Colposcopy","authors":"N. Zarei, D. Cox, P. Lane, S. Cantor, E. Atkinson, Jose-Miguel Yamal, Leonid Fradkin, D. Serachitopol, S. Lam, D. Niekerk, Dianne Miller, J. McAlpine, K. Castaneda, F. Castaneda, M. Follen, C. MacAulay","doi":"10.5220/0006148900850091","DOIUrl":"https://doi.org/10.5220/0006148900850091","url":null,"abstract":"Background: Cervical cancer develops over several years; screening and early diagnosis have decreased the incidence and mortality threefold over the last fifty years. Opportunities for the application of imaging and automation in the screening process exist in settings where resources are limited. Methods: Patients with high-grade squamous intraepithelial lesions (SIL) underwent imaging with a Multispectral Digital Colposcopy (MDC) prior to have a loop excision of the cervix. The image taken with white light was annotated by a clinician. The excised specimen was mapped by the study histopathologist blinded to the MDC data. This map was used to define areas of high grade in the excised tissue. Eleven reviewers mapped the histopathologic data into the MDC images. The reviewers’ maps were analyzed and areas of agreement were calculated. We compared the result of a boosted tree classifier with a previously developed ensemble classifier. Results: Using a boosted tree classifier we obtained a sensitivity of 95%, a specificity of 96%, and an accuracy of 96% on the training sets. When we applied the classifier to a test set, we obtained a sensitivity of 82%, a specificity of 81%, and an accuracy of 81%. The boosted tree classifier performed better than the previously developed ensemble classifier. Conclusion: Here we presented promising results which show that a boosted tree analysis on MDC images is a method that could be used as an adjunct to colposcopy and would result in greater diagnostic accuracy compared to existing methods.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123842182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Anatomical Priors for Deep 3D One-shot Segmentation","authors":"Duc Duy Pham, Gurbandurdy Dovletov, J. Pauli","doi":"10.5220/0010303101740181","DOIUrl":"https://doi.org/10.5220/0010303101740181","url":null,"abstract":"With the success of deep convolutional neural networks for semantic segmentation in the medical imaging domain, there is a high demand for labeled training data, that is often not available or expensive to acquire. Training with little data usually leads to overfitting, which prohibits the model to generalize to unseen problems. However, in the medical imaging setting, image perspectives and anatomical topology do not vary as much as in natural images, as the patient is often instructed to hold a specific posture to follow a standardized protocol. In this work we therefore investigate the one-shot segmentation capabilities of a standard 3D U-Net architecture in such setting and propose incorporating anatomical priors to increase the segmentation performance. We evaluate our proposed method on the example of liver segmentation in abdominal CT volumes.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125179028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BOLD Signal Change during Driving with Addition Task using fMRI","authors":"Ji-Hun Jo, Hyung-Sik Kim, Soon-Cheol Chung, Mi-Hyun Choi","doi":"10.5220/0007376501000103","DOIUrl":"https://doi.org/10.5220/0007376501000103","url":null,"abstract":"This paper uses a driving wheel and pedal (working as an accelerator, brake) equipped with an MRcompatible driving simulator at a speed of 80 km/h when driving and when driving while performing secondary tasks in order to observe differences in neuronal activation (BOLD signal change). The experiments consisted of three blocks, each block consisting of both a Control phase (1 min.) and a Driving phase (2 min.). During the Control phase, the drivers were instructed to look at the stop screen and not to perform driving tasks. During the Driving phase, the drivers either drove or drove while performing addition tasks at 80 km/h. The intensity of activated voxels increased in the addition task condition compared to the driving condition in insula.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128670152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}