We describe a fully automated method for hippocampal segmentation. The method uses SPM5 (http://www.fil.ion.ucl.ac.uk/spm/) software to segment the brain into grey/white matter, and spatially normalize the images to standard space. Grey matter pixels within a predefined hippocampal region in standard space are identified to segment the hippocampi. The method was validated on 36 subjects (9 each of Alzheimer's disease, dementia with Lewy bodies, vascular dementia, and healthy controls). The mean absolute difference in volume compared with manual segmentation was 11% (SD 9%). Linear regression between manual and automated volume gave V(auto) = V(manual) x 0.83 + 401 ml. The method provides an acceptable automated alternative to manual segmentation which may be of value in large studies.
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http://dx.doi.org/10.1002/hbm.20480 | DOI Listing |
J Imaging Inform Med
January 2025
College of Computer, Chongqing University, No. 55 Daxuecheng South Rd, Shapingba, 401331, Chongqing, China.
Convolutional neural networks (CNNs) have become indispensable to medical image diagnosis research, enabling the automated differentiation of diseased images from extensive medical image datasets. Due to their efficacy, these methods raise significant privacy concerns regarding patient images and diagnostic models. To address these issues, some researchers have explored privacy-preserving medical image diagnosis schemes using fully homomorphic encryption (FHE).
View Article and Find Full Text PDFPathol Res Pract
December 2024
Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy.
Pathology laboratories are currently facing remarkable issues in the management of their archives due to the ongoing increase in the production of formalin-fixed paraffin-embedded (FFPE) blocks, which is often coupled with inadequate spatial and environmental storing conditions. The manual process of storage and retrieving further increases the likelihood of human-based mistakes, wastes professionals' working time, and, ultimately, widens reports signing turn-around times. In the present work, we outline the strategies underlying the development of an automated archive at the pathology services of the University of Modena.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Miin Wu School of Computing, National Cheng Kung University, Tainan, Taiwan.
Background: Alzheimer's disease (AD) has been associated with speech and language impairment. Recent progress in the field has led to the development of automated AD detection using audio-based methods, because it has a great potential for cross-linguistic detection. In this investigation, we utilised a pretrained deep learning model to automatically detect AD, leveraging acoustic data derived from Chinese speech.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Background: This study responds to the urgent need for automated and reliable methods to detect cognitive impairments on a large scale. It leverages natural language processing (NLP) techniques to predict dementia and mild cognitive impairment (MCI) using clinical notes from electronic health records (EHR).
Method: Our study used an EHR dataset from Massachusetts General Brigham, which included clinical notes from a 2-year period (2017-2018) covering 12 types of patient encounters.
Sci Rep
January 2025
Department of Dermatology, Poznan University of Medical Sciences, Poznan, Poland.
The last decades have brought an interest in ultrasound applications in dermatology. Especially in the case of atopic dermatitis, where the formation of a subepidermal low echogenic band (SLEB) may serve as an independent indicator of the effects of treatment, the use of ultrasound is of particular interest. This study proposes and evaluates the computer-aided diagnosis method for assessing atopic dermatitis (AD).
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