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Artikel ; Online: Organization of hippocampal CA3 into correlated cell assemblies supports a stable spatial code

Liron Sheintuch / Nitzan Geva / Daniel Deitch / Alon Rubin / Yaniv Ziv

Cell Reports, Vol 42, Iss 2, Pp 112119- (2023)

2023

Abstract: Summary: Hippocampal subfield CA3 is thought to stably store memories in assemblies of recurrently connected cells functioning as a collective. However, the collective hippocampal coding properties that are unique to CA3 and how such properties ... ...

Abstract	Summary: Hippocampal subfield CA3 is thought to stably store memories in assemblies of recurrently connected cells functioning as a collective. However, the collective hippocampal coding properties that are unique to CA3 and how such properties facilitate the stability or precision of the neural code remain unclear. Here, we performed large-scale Ca2+ imaging in hippocampal CA1 and CA3 of freely behaving mice that repeatedly explored the same, initially novel environments over weeks. CA3 place cells have more precise and more stable tuning and show a higher statistical dependence with their peers compared with CA1 place cells, uncovering a cell assembly organization in CA3. Surprisingly, although tuning precision and long-term stability are correlated, cells with stronger peer dependence exhibit higher stability but not higher precision. Overall, our results expose the three-way relationship between tuning precision, long-term stability, and peer dependence, suggesting that a cell assembly organization underlies long-term storage of information in the hippocampus.
Schlagwörter	CP: Neuroscience ; Biology (General) ; QH301-705.5
Thema/Rubrik (Code)	612
Sprache	Englisch
Erscheinungsdatum	2023-02-01T00:00:00Z
Verlag	Elsevier
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Bias-free estimation of information content in temporally sparse neuronal activity.

Liron Sheintuch / Alon Rubin / Yaniv Ziv

PLoS Computational Biology, Vol 18, Iss 2, p e

2022 Band 1009832

Abstract: Applying information theoretic measures to neuronal activity data enables the quantification of neuronal encoding quality. However, when the sample size is limited, a naïve estimation of the information content typically contains a systematic ... ...

Abstract	Applying information theoretic measures to neuronal activity data enables the quantification of neuronal encoding quality. However, when the sample size is limited, a naïve estimation of the information content typically contains a systematic overestimation (upward bias), which may lead to misinterpretation of coding characteristics. This bias is exacerbated in Ca2+ imaging because of the temporal sparsity of elevated Ca2+ signals. Here, we introduce methods to correct for the bias in the naïve estimation of information content from limited sample sizes and temporally sparse neuronal activity. We demonstrate the higher accuracy of our methods over previous ones, when applied to Ca2+ imaging data recorded from the mouse hippocampus and primary visual cortex, as well as to simulated data with matching tuning properties and firing statistics. Our bias-correction methods allowed an accurate estimation of the information place cells carry about the animal's position (spatial information) and uncovered the spatial resolution of hippocampal coding. Furthermore, using our methods, we found that cells with higher peak firing rates carry higher spatial information per spike and exposed differences between distinct hippocampal subfields in the long-term evolution of the spatial code. These results could be masked by the bias when applying the commonly used naïve calculation of information content. Thus, a bias-free estimation of information content can uncover otherwise overlooked properties of the neural code.
Schlagwörter	Biology (General) ; QH301-705.5
Thema/Rubrik (Code)	310
Sprache	Englisch
Erscheinungsdatum	2022-02-01T00:00:00Z
Verlag	Public Library of Science (PLoS)
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel: Tuberculosis Chest X-Ray Image Retrieval System Using Deep Learning Based Biomarker Predictions.

Lowekamp, Bradley C / Gabrielian, Andrei / Hurt, Darrell E / Rosenthal, Alex / Yaniv, Ziv

Proceedings of SPIE--the International Society for Optical Engineering

2024 Band 12931

Abstract: The world health organization's global tuberculosis (TB) report for 2022 identifies TB, with an estimated 1.6 million, as a leading cause of death. The number of new cases has risen since 2020, particularly the number of new drug-resistant cases, ... ...

Abstract	The world health organization's global tuberculosis (TB) report for 2022 identifies TB, with an estimated 1.6 million, as a leading cause of death. The number of new cases has risen since 2020, particularly the number of new drug-resistant cases, estimated at 450,000 in 2021. This is concerning, as treatment of patients with drug resistant TB is complex and may not always be successful. The NIAID TB Portals program is an international consortium with a primary focus on patient centric data collection and analysis for drug resistant TB. The data includes images, their associated radiological findings, clinical records, and socioeconomic information. This work describes a TB Portals' Chest X-ray based image retrieval system which enables precision medicine. An input image is used to retrieve similar images and the associated patient specific information, thus facilitating inspection of outcomes and treatment regimens from comparable patients. Image similarity is defined using clinically relevant biomarkers: gender, age, body mass index (BMI), and the percentage of lung affected per sextant. The biomarkers are predicted using variations of the DenseNet169 convolutional neural network. A multi-task approach is used to predict gender, age and BMI incorporating transfer learning from an initial training on the NIH Clinical Center CXR dataset to the TB portals dataset. The resulting gender AUC, age and BMI mean absolute errors were 0.9854, 4.03years and
Sprache	Englisch
Erscheinungsdatum	2024-04-02
Erscheinungsland	United States
Dokumenttyp	Journal Article
ISSN	0277-786X
ISSN	0277-786X
DOI	10.1117/12.3006848
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Automated Pulmonary Tuberculosis Severity Assessment on Chest X-rays.

Kantipudi, Karthik / Gu, Jingwen / Bui, Vy / Yu, Hang / Jaeger, Stefan / Yaniv, Ziv

Journal of imaging informatics in medicine

2024

Abstract: According to the 2022 World Health Organization's Global Tuberculosis (TB) report, an estimated 10.6 million people fell ill with TB, and 1.6 million died from the disease in 2021. In addition, 2021 saw a reversal of a decades-long trend of declining TB ... ...

Abstract	According to the 2022 World Health Organization's Global Tuberculosis (TB) report, an estimated 10.6 million people fell ill with TB, and 1.6 million died from the disease in 2021. In addition, 2021 saw a reversal of a decades-long trend of declining TB infections and deaths, with an estimated increase of 4.5% in the number of people who fell ill with TB compared to 2020, and an estimated yearly increase of 450,000 cases of drug resistant TB. Estimating the severity of pulmonary TB using frontal chest X-rays (CXR) can enable better resource allocation in resource constrained settings and monitoring of treatment response, enabling prompt treatment modifications if disease severity does not decrease over time. The Timika score is a clinically used TB severity score based on a CXR reading. This work proposes and evaluates three deep learning-based approaches for predicting the Timika score with varying levels of explainability. The first approach uses two deep learning-based models, one to explicitly detect lesion regions using YOLOV5n and another to predict the presence of cavitation using DenseNet121, which are then utilized in score calculation. The second approach uses a DenseNet121-based regression model to directly predict the affected lung percentage and another to predict cavitation presence using a DenseNet121-based classification model. Finally, the third approach directly predicts the Timika score using a DenseNet121-based regression model. The best performance is achieved by the second approach with a mean absolute error of 13-14% and a Pearson correlation of 0.7-0.84 using three held-out datasets for evaluating generalization.
Sprache	Englisch
Erscheinungsdatum	2024-04-08
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ISSN	2948-2933
ISSN (online)	2948-2933
DOI	10.1007/s10278-024-01052-7
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Konferenzbeitrag: Image-guided procedures, robotic interventions, and modeling

Yaniv, Ziv R

medical imaging 2015 ; 22 - 24 February 2015, Orlando, Florida, United States ; [SPIE Image-Guided Procedures, Robotic Interventions, and Modeling Conference proceeding]

(Proceedings of SPIE ; 9415 ; Progress in biomedical optics and imaging ; Vol. 16, No. 43)

2015

Titelvarianten	Medical imaging 2015: image-guided procedures, robotic interventions, and modeling
Veranstaltung/Kongress	SPIE Image-Guided Procedures, Robotic Interventions, and Modeling Conference (2015.02.22-24, OrlandoFla.) ; SPIE Medical Imaging (2015.02.22-24, OrlandoFla.)
Verfasserangabe	sponsored by SPIE. Ziv R. Yaniv, ... ed
Serientitel	Proceedings of SPIE ; 9415 Progress in biomedical optics and imaging ; Vol. 16, No. 43
Sprache	Englisch
Umfang	Getr. Zählung [ca. 750 S.], Ill., graph. Darst.
Verlag	SPIE
Erscheinungsort	Bellingham, Wash
Dokumenttyp	Buch ; Konferenzbeitrag
ISBN	9781628415056 ; 1628415053
Datenquelle	Katalog der Technische Informationsbibliothek Hannover

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Buch ; Konferenzbeitrag: Medical imaging 2014: image-guided procedures, robotic interventions, and modeling

Yaniv, Ziv R

18 - 20 February 2014, San Diego, California, United States

(Proceedings of SPIE ; 9036 ; Progress in biomedical optics and imaging ; Vol. 15, No. 37)

2014

Titelvarianten	Image-guided procedures, robotic interventions, and modeling
Veranstaltung/Kongress	Image-Guided Procedures, Robotic Interventions, and Modeling Conference (2014.02.18-20, SanDiegoCalif.) ; SPIE medical imaging (2014.02.15-20, SanDiegoCalif.)
Verfasserangabe	sponsored by SPIE. Ziv R. Yaniv ..., ed
Serientitel	Proceedings of SPIE ; 9036 Progress in biomedical optics and imaging ; Vol. 15, No. 37
Sprache	Englisch
Umfang	Getr. Zählung, [ca. 800 S.], Ill., graph. Darst.
Verlag	SPIE
Erscheinungsort	Bellingham, Wash
Dokumenttyp	Buch ; Konferenzbeitrag
ISBN	9780819498298 ; 0819498297
Datenquelle	Katalog der Technische Informationsbibliothek Hannover

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Artikel: Evaluation of spherical fiducial localization in C-arm cone-beam CT using patient data.

Yaniv, Ziv

Medical physics

2010 Band 37, Heft 10, Seite(n) 5298–5305

Abstract: Purpose: C-arm based cone-beam CT (CBCT) has been recently introduced as an in-situ 3D soft tissue imaging modality. When combined with image-guided navigation, it provides a streamlined clinical workflow with, potentially, improved interventional ... ...

Abstract	Purpose: C-arm based cone-beam CT (CBCT) has been recently introduced as an in-situ 3D soft tissue imaging modality. When combined with image-guided navigation, it provides a streamlined clinical workflow with, potentially, improved interventional accuracy. A key component in these systems is image to patient registration. The most common registration method relies on fiducial markers placed on the patient's skin. The fiducials are localized in the volumetric image and in the interventional environment. When using C-arm CBCT, the small spatial extent of the volumetric reconstruction makes this registration approach challenging, as the volume must include both the anatomy of interest and the fiducials. The authors have previously proposed a semiautomatic localization approach that addresses this challenge, with evaluation carried out using anthropomorphic phantoms. To truly evaluate the algorithm's utility, the evaluation must be carried out using clinical data. In this article, the authors extend the evaluation of the approach to data sets acquired in a clinical trial. Methods: Nine CBCT data sets were obtained in three interventional radiology procedures as part of a clinical trial evaluating a commercial navigation system. Fiducials were localized in the volumetric coordinate system directly from the projection images using the evaluated localization approach. Localization was assessed using two quality measures fiducial registration error to quantify precision and fiducial localization error to quantify accuracy. The fiducials used in this study are 6 mm spheres embedded in a custom registration phantom used by the navigation system. Results: In all cases, the proposed approach was able to localize all five fiducial markers embedded in the registration phantom. The approach's mean (std) fiducial registration error was 0.29 (0.13) mm. The mean (std) localization difference as compared to direct volumetric localization was 0.82 (0.34) mm. Conclusions: Based on the current evaluation using data from clinical cases, the authors conclude that the localization approach is sufficiently accurate for use in thoracic-abdominal interventions, and that it can simplify the current workflow while reducing cumulative radiation to the patient due to repeated CBCT scans.
Mesh-Begriff(e)	Algorithms ; Biophysical Phenomena ; Cone-Beam Computed Tomography/instrumentation ; Cone-Beam Computed Tomography/statistics & numerical data ; Electromagnetic Phenomena ; Humans ; Imaging, Three-Dimensional ; Phantoms, Imaging ; Radiographic Image Interpretation, Computer-Assisted
Sprache	Englisch
Erscheinungsdatum	2010-10
Erscheinungsland	United States
Dokumenttyp	Evaluation Studies ; Journal Article
ZDB-ID	188780-4
ISSN	0094-2405
ISSN	0094-2405
DOI	10.1118/1.3475941
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis.

Bui, Vy C B / Yaniv, Ziv / Harris, Michael / Yang, Feng / Kantipudi, Karthik / Hurt, Darrell / Rosenthal, Alex / Jaeger, Stefan

IEEE access : practical innovations, open solutions

2023 Band 11, Seite(n) 84228–84240

Abstract: Tuberculosis (TB) drug resistance is a worldwide public health problem. It decreases the likelihood of a positive outcome for the individual patient and increases the likelihood of disease spread. Therefore, early detection of TB drug resistance is ... ...

Abstract	Tuberculosis (TB) drug resistance is a worldwide public health problem. It decreases the likelihood of a positive outcome for the individual patient and increases the likelihood of disease spread. Therefore, early detection of TB drug resistance is crucial for improving outcomes and controlling disease transmission. While drug-sensitive tuberculosis cases are declining worldwide because of effective treatment, the threat of drug-resistant tuberculosis is growing, and the success rate of drug-resistant tuberculosis treatment is only around 60%. The TB Portals program provides a publicly accessible repository of TB case data with an emphasis on collecting drug-resistant cases. The dataset includes multi-modal information such as socioeconomic/geographic data, clinical characteristics, pathogen genomics, and radiological features. The program is an international collaboration whose participants are typically under a substantial burden of drug-resistant tuberculosis, with data collected from standard clinical care provided to the patients. Consequentially, the TB Portals dataset is heterogenous in nature, with data representing multiple treatment centers in different countries and containing cross-domain information. This study presents the challenges and methods used to address them when working with this real-world dataset. Our goal was to evaluate whether combining radiological features derived from a chest X-ray of the host and genomic features from the pathogen can potentially improve the identification of the drug susceptibility type, drug-sensitive (DS-TB) or drug-resistant (DR-TB), and the length of the first successful drug regimen. To perform these studies, significantly imbalanced data needed to be processed, which included a much larger number of DR-TB cases than DS-TB, many more cases with radiological findings than genomic ones, and the sparse high dimensional nature of the genomic information. Three evaluation studies were carried out. First, the DR-TB/DS-TB classification model achieved an average accuracy of 92.4% when using genomic features alone or when combining radiological and genomic features. Second, the regression model for the length of the first successful treatment had a relative error of 53.5% using radiological features, 25.6% using genomic features, and 22.0% using both radiological and genomic features. Finally, the relative error of the third regression model predicting the length of the first treatment using the most common drug combination varied depending on the feature type used. When using radiological features alone, the relative error was 17.8%. For genomic features alone, the relative error increased to 19.9%. The model had a relative error of 19.0% when both radiological and genomic features were combined. Although combining radiological and genomic features did not improve upon the use of genomic features when classifying DR-TB/DS-TB, the combination of the two feature types improved the relative error of the predictive model for the length of the first successful treatment. Furthermore, the regression model trained on radiological features achieved the best performance when predicting the treatment length of the most common drug combination.
Sprache	Englisch
Erscheinungsdatum	2023-07-25
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2687964-5
ISSN	2169-3536
ISSN	2169-3536
DOI	10.1109/access.2023.3298750
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning.

Dangi, Shusil / Yaniv, Ziv / Linte, Cristian A

Statistical atlases and computational models of the heart. STACOM (Workshop)

2019 Band 11395, Seite(n) 21–31

Abstract: Segmentation of the left ventricle and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases. Traditionally, the two tasks have been tackled independently. Here we propose a ... ...

Abstract	Segmentation of the left ventricle and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases. Traditionally, the two tasks have been tackled independently. Here we propose a convolutional neural network based multi-task learning approach to perform both tasks simultaneously, such that, the network learns better representation of the data with improved generalization performance. Probabilistic formulation of the problem enables learning the task uncertainties during the training, which are used to automatically compute the weights for the tasks. We performed a five fold cross-validation of the myocardium segmentation obtained from the proposed multi-task network on 97 patient 4-dimensional cardiac cine-MRI datasets available through the STA-COM LV segmentation challenge against the provided gold-standard myocardium segmentation, obtaining a Dice overlap of 0.849 ± 0.036 and mean surface distance of 0.274 ± 0.083 mm, while simultaneously estimating the myocardial area with mean absolute difference error of 205 ± 198 mm
Sprache	Englisch
Erscheinungsdatum	2019-02-14
Erscheinungsland	Germany
Dokumenttyp	Journal Article
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Papers from the 17th Joint Workshop on Augmented Environments for Computer Assisted Interventions at MICCAI 2023: Guest Editors' Foreword.

Linte, Cristian A / Yaniv, Ziv / Chen, Elvis / Dou, Qi / Drouin, Simon / Kalia, Megha / Kersten-Oertel, Marta / McLeod, Jonathan / Sarikaya, Duygu

Healthcare technology letters

2024 Band 11, Heft 2-3, Seite(n) 31–32

Sprache	Englisch
Erscheinungsdatum	2024-02-28
Erscheinungsland	England
Dokumenttyp	Editorial
ZDB-ID	2782924-8
ISSN	2053-3713
ISSN	2053-3713
DOI	10.1049/htl2.12082
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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