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1.
J Biomed Opt ; 29(Suppl 2): S22702, 2025 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-38434231

RESUMO

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.


Assuntos
Técnicas Histológicas , Microscopia , Animais , Citometria de Fluxo , Processamento de Imagem Assistida por Computador
2.
F1000Res ; 13: 691, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38962692

RESUMO

Background: Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blotchy appearance and decreased resolution for subtle contrasts. The deep-learning image reconstruction (DLIR) algorithm, which integrates a convolutional neural network (CNN) into the reconstruction process, generates high-quality images with minimal noise. Hence, the objective of this study was to assess the IQ of the Precise Image (DLIR) and the IR technique (iDose 4) for the NCCT brain. Methods: This is a prospective study. Thirty patients who underwent NCCT brain were included. The images were reconstructed using DLIR-standard and iDose 4. Qualitative IQ analysis parameters, such as overall image quality (OQ), subjective image noise (SIN), and artifacts, were measured. Quantitative IQ analysis parameters such as Computed Tomography (CT) attenuation (HU), image noise (IN), posterior fossa index (PFI), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in the basal ganglia (BG) and centrum-semiovale (CSO) were measured. Paired t-tests were performed for qualitative and quantitative IQ analyses between the iDose 4 and DLIR-standard. Kappa statistics were used to assess inter-observer agreement for qualitative analysis. Results: Quantitative IQ analysis showed significant differences (p<0.05) in IN, SNR, and CNR between the iDose 4 and DLIR-standard at the BG and CSO levels. IN was reduced (41.8-47.6%), SNR (65-82%), and CNR (68-78.8%) were increased with DLIR-standard. PFI was reduced (27.08%) the DLIR-standard. Qualitative IQ analysis showed significant differences (p<0.05) in OQ, SIN, and artifacts between the DLIR standard and iDose 4. The DLIR standard showed higher qualitative IQ scores than the iDose 4. Conclusion: DLIR standard yielded superior quantitative and qualitative IQ compared to the IR technique (iDose4). The DLIR-standard significantly reduced the IN and artifacts compared to iDose 4 in the NCCT brain.


Assuntos
Encéfalo , Aprendizado Profundo , Processamento de Imagem Assistida por Computador , Tomografia Computadorizada por Raios X , Humanos , Projetos Piloto , Feminino , Tomografia Computadorizada por Raios X/métodos , Masculino , Estudos Prospectivos , Pessoa de Meia-Idade , Encéfalo/diagnóstico por imagem , Adulto , Processamento de Imagem Assistida por Computador/métodos , Idoso , Razão Sinal-Ruído , Algoritmos
3.
Clin Nucl Med ; 49(8): 727-732, 2024 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-38967505

RESUMO

PURPOSE: The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement. PATIENTS AND METHODS: Manually drawn ROIs (mROIs) on end-systolic and end-diastolic images were extracted from reports in a Picture Archiving and Communications System. To reduce observer variability, preprocessed ROIs (pROIs) were delineated using a 41% threshold of the maximal pixel counts of the extracted mROIs and were labeled as ground-truth. Background ROIs were automatically created using an algorithm to identify areas with minimum counts within specified probability areas around the end-systolic ROI. A 2-dimensional U-Net convolutional neural network architecture was trained to generate deep learning-based ROIs (dlROIs) from pROIs. The model's performance was evaluated using Lin's concordance correlation coefficient (CCC). Bland-Altman plots were used to assess bias and 95% limits of agreement. RESULTS: A total of 41,462 scans (19,309 patients) were included. Strong concordance was found between LVEF measurements from dlROIs and pROIs (CCC = 85.6%; 95% confidence interval, 85.4%-85.9%), and between LVEF measurements from dlROIs and mROIs (CCC = 86.1%; 95% confidence interval, 85.8%-86.3%). In the Bland-Altman analysis, the mean differences and 95% limits of agreement of the LVEF measurements were -0.6% and -6.6% to 5.3%, respectively, for dlROIs and pROIs, and -0.4% and -6.3% to 5.4% for dlROIs and mROIs, respectively. In 37,537 scans (91%), the absolute LVEF difference between dlROIs and mROIs was <5%. CONCLUSIONS: Our 2-dimensional U-Net convolutional neural network architecture showed excellent performance in generating LV ROIs from equilibrium radionuclide angiography scans. It may enhance the convenience and reproducibility of LVEF measurements.


Assuntos
Redes Neurais de Computação , Humanos , Automação , Angiocardiografia , Masculino , Processamento de Imagem Assistida por Computador/métodos , Feminino , Pessoa de Meia-Idade , Volume Sistólico , Idoso , Imagem do Acúmulo Cardíaco de Comporta/métodos , Aprendizado Profundo
4.
Sci Rep ; 14(1): 15317, 2024 07 03.
Artigo em Inglês | MEDLINE | ID: mdl-38961218

RESUMO

The hippocampus is a critical component of the brain and is associated with many neurological disorders. It can be further subdivided into several subfields, and accurate segmentation of these subfields is of great significance for diagnosis and research. However, the structures of hippocampal subfields are irregular and have complex boundaries, and their voxel values are close to surrounding brain tissues, making the segmentation task highly challenging. Currently, many automatic segmentation tools exist for hippocampal subfield segmentation, but they suffer from high time costs and low segmentation accuracy. In this paper, we propose a new dual-branch segmentation network structure (DSnet) based on deep learning for hippocampal subfield segmentation. While traditional convolutional neural network-based methods are effective in capturing hierarchical structures, they struggle to establish long-term dependencies. The DSnet integrates the Transformer architecture and a hybrid attention mechanism, enhancing the network's global perceptual capabilities. Moreover, the dual-branch structure of DSnet leverages the segmentation results of the hippocampal region to facilitate the segmentation of its subfields. We validate the efficacy of our algorithm on the public Kulaga-Yoskovitz dataset. Experimental results indicate that our method is more effective in segmenting hippocampal subfields than conventional single-branch network structures. Compared to the classic 3D U-Net, our proposed DSnet improves the average Dice accuracy of hippocampal subfield segmentation by 0.57%.


Assuntos
Algoritmos , Aprendizado Profundo , Hipocampo , Redes Neurais de Computação , Hipocampo/diagnóstico por imagem , Hipocampo/anatomia & histologia , Humanos , Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos
5.
Sci Rep ; 14(1): 15344, 2024 07 03.
Artigo em Inglês | MEDLINE | ID: mdl-38961220

RESUMO

Decreased myocardial capillary density has been reported as an important histopathological feature associated with various heart disorders. Quantitative assessment of cardiac capillarization typically involves double immunostaining of cardiomyocytes (CMs) and capillaries in myocardial slices. In contrast, single immunostaining of basement membrane protein is a straightforward approach to simultaneously label CMs and capillaries, presenting fewer challenges in background staining. However, subsequent image analysis always requires expertise and laborious manual work to identify and segment CMs/capillaries. Here, we developed an image analysis tool, AutoQC, for automatic identification and segmentation of CMs and capillaries in immunofluorescence images of basement membrane. Commonly used capillarization-related measurements can be derived from segmentation results. By leveraging the power of a pre-trained segmentation model (Segment Anything Model, SAM) via prompt engineering, the training of AutoQC required only a small dataset with bounding box annotations instead of pixel-wise annotations. AutoQC outperformed SAM (without prompt engineering) and YOLOv8-Seg, a state-of-the-art instance segmentation model, in both instance segmentation and capillarization assessment. Thus, AutoQC, featuring a weakly supervised algorithm, enables automatic segmentation and high-throughput, high-accuracy capillarization assessment in basement-membrane-immunostained myocardial slices. This approach reduces the training workload and eliminates the need for manual image analysis once AutoQC is trained.


Assuntos
Membrana Basal , Processamento de Imagem Assistida por Computador , Miocárdio , Miócitos Cardíacos , Membrana Basal/metabolismo , Animais , Miócitos Cardíacos/metabolismo , Miocárdio/metabolismo , Miocárdio/patologia , Processamento de Imagem Assistida por Computador/métodos , Capilares/metabolismo , Algoritmos , Camundongos , Vasos Coronários/metabolismo , Vasos Coronários/patologia
6.
J Biomed Opt ; 29(7): 076002, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38966847

RESUMO

Significance: Optical coherence tomography has great utility for capturing dynamic processes, but such applications are particularly data-intensive. Samples such as biological tissues exhibit temporal features at varying time scales, which makes data reduction challenging. Aim: We propose a method for capturing short- and long-term correlations of a sample in a compressed way using non-uniform temporal sampling to reduce scan time and memory overhead. Approach: The proposed method separates the relative contributions of white noise, fluctuating features, and stationary features. The method is demonstrated on mammary epithelial cell spheroids in three-dimensional culture for capturing intracellular motility without loss of signal integrity. Results: Results show that the spatial patterns of motility are preserved and that hypothesis tests of spheroids treated with blebbistatin, a motor protein inhibitor, are unchanged with up to eightfold compression. Conclusions: The ability to measure short- and long-term correlations compressively will enable new applications in (3+1)D imaging and high-throughput screening.


Assuntos
Tomografia de Coerência Óptica , Tomografia de Coerência Óptica/métodos , Humanos , Esferoides Celulares/efeitos dos fármacos , Movimento Celular/fisiologia , Movimento Celular/efeitos dos fármacos , Processamento de Imagem Assistida por Computador/métodos , Células Epiteliais/efeitos dos fármacos , Algoritmos , Compostos Heterocíclicos de 4 ou mais Anéis
7.
PeerJ ; 12: e17557, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38952993

RESUMO

Imagery has become one of the main data sources for investigating seascape spatial patterns. This is particularly true in deep-sea environments, which are only accessible with underwater vehicles. On the one hand, using collaborative web-based tools and machine learning algorithms, biological and geological features can now be massively annotated on 2D images with the support of experts. On the other hand, geomorphometrics such as slope or rugosity derived from 3D models built with structure from motion (sfm) methodology can then be used to answer spatial distribution questions. However, precise georeferencing of 2D annotations on 3D models has proven challenging for deep-sea images, due to a large mismatch between navigation obtained from underwater vehicles and the reprojected navigation computed in the process of building 3D models. In addition, although 3D models can be directly annotated, the process becomes challenging due to the low resolution of textures and the large size of the models. In this article, we propose a streamlined, open-access processing pipeline to reproject 2D image annotations onto 3D models using ray tracing. Using four underwater image datasets, we assessed the accuracy of annotation reprojection on 3D models and achieved successful georeferencing to centimetric accuracy. The combination of photogrammetric 3D models and accurate 2D annotations would allow the construction of a 3D representation of the landscape and could provide new insights into understanding species microdistribution and biotic interactions.


Assuntos
Imageamento Tridimensional , Imageamento Tridimensional/métodos , Algoritmos , Aprendizado de Máquina , Processamento de Imagem Assistida por Computador/métodos , Oceanos e Mares
8.
IEEE J Biomed Health Inform ; 28(7): 4170-4183, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38954557

RESUMO

Efficient medical image segmentation aims to provide accurate pixel-wise predictions with a lightweight implementation framework. However, existing lightweight networks generally overlook the generalizability of the cross-domain medical segmentation tasks. In this paper, we propose Generalizable Knowledge Distillation (GKD), a novel framework for enhancing the performance of lightweight networks on cross-domain medical segmentation by generalizable knowledge distillation from powerful teacher networks. Considering the domain gaps between different medical datasets, we propose the Model-Specific Alignment Networks (MSAN) to obtain the domain-invariant representations. Meanwhile, a customized Alignment Consistency Training (ACT) strategy is designed to promote the MSAN training. Based on the domain-invariant vectors in MSAN, we propose two generalizable distillation schemes, Dual Contrastive Graph Distillation (DCGD) and Domain-Invariant Cross Distillation (DICD). In DCGD, two implicit contrastive graphs are designed to model the intra-coupling and inter-coupling semantic correlations. Then, in DICD, the domain-invariant semantic vectors are reconstructed from two networks (i.e., teacher and student) with a crossover manner to achieve simultaneous generalization of lightweight networks, hierarchically. Moreover, a metric named Fréchet Semantic Distance (FSD) is tailored to verify the effectiveness of the regularized domain-invariant features. Extensive experiments conducted on the Liver, Retinal Vessel and Colonoscopy segmentation datasets demonstrate the superiority of our method, in terms of performance and generalization ability on lightweight networks.


Assuntos
Processamento de Imagem Assistida por Computador , Humanos , Processamento de Imagem Assistida por Computador/métodos , Algoritmos , Redes Neurais de Computação , Bases de Dados Factuais , Aprendizado Profundo
9.
J Contemp Dent Pract ; 25(4): 320-325, 2024 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-38956845

RESUMO

AIM: The aim of the present research was to assess the mesiodistal angulation of the maxillary anterior teeth utilizing Image J computer software, a Profile projector, and a Custom-made jig. MATERIALS AND METHODS: A total of 34 subjects (17 males and 17 females) were chosen from a group of 18-30 years old with bilateral Angle Class I molars and canine relationships. One manual approach (Custom-made jig) and two digital methods (J computer software, a Profile projector) were used to record the mesiodistal angulation in incisal view. The individuals had alginate impressions made, and a facebow was used to capture the maxilla's spatial relationship with the cranium. The articulated cast with the help of mounting ring moved to the specially customized jig, then the angulations was measured in the incisal view after the casts were placed in a semi-adjustable articulator. Data were recorded and statistically analyzed. RESULTS: The mesiodistal angulation in the incisal view via three methods between the 17 males and 17 females has statistically significant different. Although the mesiodistal angulation for maxillary lateral incisor and canine did not show any statistically significant difference, the maximum and minimum values obtained were always greater in males in comparison with the females. This indicates that the positions of six maxillary anterior teeth in the males resulted in the creation of upward sweep of incisal edges of central and lateral incisors which was also referred to as "smiling line" producing masculine surface anatomy more squared and vigorous while feminine surface anatomy being more rounded, soft, and pleasant. There was no statistically significant difference between the right and left sides, indicating bilateral arch symmetry and the symmetrical place of the right teeth compared with the left side's corresponding teeth. CONCLUSION: On conclusion, according to the current study's findings, all three approaches can measure the mesiodistal angulations of maxillary anterior teeth in incisal view with clinically acceptable accuracy. The digital methods, which included using the Image J computer software and the profile projector, achieved more accurate results than the manual method. CLINICAL SIGNIFICANCE: The outcomes of this study's mesiodistal angulations can be used as a reference for placing teeth in both fully and partially edentulous conditions. This study contributes to a better understanding of the importance of achieving the ideal occlusion in the Indian population by placing the maxillary anterior teeth at the proper mesiodistal angulation. How to cite this article: Shadaksharappa SH, Lahiri B, Kamath AG, et al. Evaluation of Mesiodistal Angulation of Maxillary Anterior Teeth in Incisal View Using Manual and Digital Methods: An In Vivo Study. J Contemp Dent Pract 2024;25(4):320-325.


Assuntos
Incisivo , Maxila , Humanos , Masculino , Feminino , Maxila/anatomia & histologia , Adolescente , Incisivo/anatomia & histologia , Adulto Jovem , Adulto , Software , Processamento de Imagem Assistida por Computador/métodos , Dente Canino/anatomia & histologia
10.
Sci Rep ; 14(1): 14993, 2024 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-38951574

RESUMO

Spinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain for many diseases and conditions. However, interpreting clinically useful information from these scans can be challenging, time-consuming and hard to reproduce across different radiologists. In this paper, we alleviate these problems by introducing a multi-stage automated pipeline for analysing spinal MR scans. This pipeline first detects and labels vertebral bodies across several commonly used sequences (e.g. T1w, T2w and STIR) and fields of view (e.g. lumbar, cervical, whole spine). Using these detections it then performs automated diagnosis for several spinal disorders, including intervertebral disc degenerative changes in T1w and T2w lumbar scans, and spinal metastases, cord compression and vertebral fractures. To achieve this, we propose a new method of vertebrae detection and labelling, using vector fields to group together detected vertebral landmarks and a language-modelling inspired beam search to determine the corresponding levels of the detections. We also employ a new transformer-based architecture to perform radiological grading which incorporates context from multiple vertebrae and sequences, as a real radiologist would. The performance of each stage of the pipeline is tested in isolation on several clinical datasets, each consisting of 66 to 421 scans. The outputs are compared to manual annotations of expert radiologists, demonstrating accurate vertebrae detection across a range of scan parameters. Similarly, the model's grading predictions for various types of disc degeneration and detection of spinal metastases closely match those of an expert radiologist. To aid future research, our code and trained models are made publicly available.


Assuntos
Imageamento por Ressonância Magnética , Humanos , Imageamento por Ressonância Magnética/métodos , Doenças da Coluna Vertebral/diagnóstico por imagem , Doenças da Coluna Vertebral/patologia , Coluna Vertebral/diagnóstico por imagem , Coluna Vertebral/patologia , Degeneração do Disco Intervertebral/diagnóstico por imagem , Degeneração do Disco Intervertebral/patologia , Processamento de Imagem Assistida por Computador/métodos , Interpretação de Imagem Assistida por Computador/métodos
11.
Sci Rep ; 14(1): 14995, 2024 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-38951630

RESUMO

Transmission electron microscopy (TEM) is an imaging technique used to visualize and analyze nano-sized structures and objects such as virus particles. Light microscopy can be used to diagnose diseases or characterize e.g. blood cells. Since samples under microscopes exhibit certain symmetries, such as global rotation invariance, equivariant neural networks are presumed to be useful. In this study, a baseline convolutional neural network is constructed in the form of the commonly used VGG16 classifier. Thereafter, it is modified to be equivariant to the p4 symmetry group of rotations of multiples of 90° using group convolutions. This yields a number of benefits on a TEM virus dataset, including higher top validation set accuracy by on average 7.6% and faster convergence during training by on average 23.1% of that of the baseline. Similarly, when training and testing on images of blood cells, the convergence time for the equivariant neural network is 7.9% of that of the baseline. From this it is concluded that augmentation strategies for rotation can be skipped. Furthermore, when modelling the accuracy versus amount of TEM virus training data with a power law, the equivariant network has a slope of - 0.43 compared to - 0.26 of the baseline. Thus the equivariant network learns faster than the baseline when more training data is added. This study extends previous research on equivariant neural networks applied to images which exhibit symmetries to isometric transformations.


Assuntos
Microscopia Eletrônica de Transmissão , Redes Neurais de Computação , Microscopia Eletrônica de Transmissão/métodos , Processamento de Imagem Assistida por Computador/métodos , Algoritmos , Rotação , Humanos
12.
Cell Metab ; 36(7): 1482-1493.e7, 2024 Jul 02.
Artigo em Inglês | MEDLINE | ID: mdl-38959862

RESUMO

Although human core body temperature is known to decrease with age, the age dependency of facial temperature and its potential to indicate aging rate or aging-related diseases remains uncertain. Here, we collected thermal facial images of 2,811 Han Chinese individuals 20-90 years old, developed the ThermoFace method to automatically process and analyze images, and then generated thermal age and disease prediction models. The ThermoFace deep learning model for thermal facial age has a mean absolute deviation of about 5 years in cross-validation and 5.18 years in an independent cohort. The difference between predicted and chronological age is highly associated with metabolic parameters, sleep time, and gene expression pathways like DNA repair, lipolysis, and ATPase in the blood transcriptome, and it is modifiable by exercise. Consistently, ThermoFace disease predictors forecast metabolic diseases like fatty liver with high accuracy (AUC > 0.80), with predicted disease probability correlated with metabolic parameters.


Assuntos
Envelhecimento , Face , Doenças Metabólicas , Humanos , Pessoa de Meia-Idade , Idoso , Adulto , Masculino , Feminino , Idoso de 80 Anos ou mais , Adulto Jovem , Aprendizado Profundo , Temperatura Corporal , Processamento de Imagem Assistida por Computador
13.
Sci Rep ; 14(1): 15462, 2024 07 05.
Artigo em Inglês | MEDLINE | ID: mdl-38965267

RESUMO

Facioscapulohumeral muscular dystrophy (FSHD) affects roughly 1 in 7500 individuals. While at the population level there is a general pattern of affected muscles, there is substantial heterogeneity in muscle expression across- and within-patients. There can also be substantial variation in the pattern of fat and water signal intensity within a single muscle. While quantifying individual muscles across their full length using magnetic resonance imaging (MRI) represents the optimal approach to follow disease progression and evaluate therapeutic response, the ability to automate this process has been limited. The goal of this work was to develop and optimize an artificial intelligence-based image segmentation approach to comprehensively measure muscle volume, fat fraction, fat fraction distribution, and elevated short-tau inversion recovery signal in the musculature of patients with FSHD. Intra-rater, inter-rater, and scan-rescan analyses demonstrated that the developed methods are robust and precise. Representative cases and derived metrics of volume, cross-sectional area, and 3D pixel-maps demonstrate unique intramuscular patterns of disease. Future work focuses on leveraging these AI methods to include upper body output and aggregating individual muscle data across studies to determine best-fit models for characterizing progression and monitoring therapeutic modulation of MRI biomarkers.


Assuntos
Inteligência Artificial , Progressão da Doença , Imageamento por Ressonância Magnética , Distrofia Muscular Facioescapuloumeral , Humanos , Distrofia Muscular Facioescapuloumeral/diagnóstico por imagem , Distrofia Muscular Facioescapuloumeral/patologia , Imageamento por Ressonância Magnética/métodos , Masculino , Feminino , Pessoa de Meia-Idade , Adulto , Músculo Esquelético/diagnóstico por imagem , Músculo Esquelético/patologia , Processamento de Imagem Assistida por Computador/métodos
14.
Sci Rep ; 14(1): 15402, 2024 07 04.
Artigo em Inglês | MEDLINE | ID: mdl-38965305

RESUMO

The diagnosis of leukemia is a serious matter that requires immediate and accurate attention. This research presents a revolutionary method for diagnosing leukemia using a Capsule Neural Network (CapsNet) with an optimized design. CapsNet is a cutting-edge neural network that effectively captures complex features and spatial relationships within images. To improve the CapsNet's performance, a Modified Version of Osprey Optimization Algorithm (MOA) has been utilized. Thesuggested approach has been tested on the ALL-IDB database, a widely recognized dataset for leukemia image classification. Comparative analysis with various machine learning techniques, including Combined combine MobilenetV2 and ResNet18 (MBV2/Res) network, Depth-wise convolution model, a hybrid model that combines a genetic algorithm with ResNet-50V2 (ResNet/GA), and SVM/JAYA demonstrated the superiority of our method in different terms. As a result, the proposed method is a robust and powerful tool for diagnosing leukemia from medical images.


Assuntos
Algoritmos , Leucemia , Redes Neurais de Computação , Humanos , Leucemia/diagnóstico por imagem , Aprendizado de Máquina , Processamento de Imagem Assistida por Computador/métodos , Bases de Dados Factuais
15.
PLoS One ; 19(7): e0305809, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38954704

RESUMO

Chromatin exhibits non-random distribution within the nucleus being arranged into discrete domains that are spatially organized throughout the nuclear space. Both the spatial distribution and structural rearrangement of chromatin domains in the nucleus depend on epigenetic modifications of DNA and/or histones and structural elements such as the nuclear envelope. These components collectively contribute to the organization and rearrangement of chromatin domains, thereby influencing genome architecture and functional regulation. This study develops an innovative, user-friendly, ImageJ-based plugin, called IsoConcentraChromJ, aimed quantitatively delineating the spatial distribution of chromatin regions in concentric patterns. The IsoConcentraChromJ can be applied to quantitative chromatin analysis in both two- and three-dimensional spaces. After DNA and histone staining with fluorescent probes, high-resolution images of nuclei have been obtained using advanced fluorescence microscopy approaches, including confocal and stimulated emission depletion (STED) microscopy. IsoConcentraChromJ workflow comprises the following sequential steps: nucleus segmentation, thresholding, masking, normalization, and trisection with specified ratios for either 2D or 3D acquisitions. The effectiveness of the IsoConcentraChromJ has been validated and demonstrated using experimental datasets consisting in nuclei images of pre-adipocytes and mature adipocytes, encompassing both 2D and 3D imaging. The outcomes allow to characterize the nuclear architecture by calculating the ratios between specific concentric nuclear areas/volumes of acetylated chromatin with respect to total acetylated chromatin and/or total DNA. The novel IsoConcentrapChromJ plugin could represent a valuable resource for researchers investigating the rearrangement of chromatin architecture driven by epigenetic mechanisms using nuclear images obtained by different fluorescence microscopy methods.


Assuntos
Núcleo Celular , Cromatina , Microscopia de Fluorescência , Cromatina/metabolismo , Cromatina/genética , Núcleo Celular/metabolismo , Núcleo Celular/genética , Animais , Camundongos , Microscopia de Fluorescência/métodos , Humanos , Histonas/metabolismo , Histonas/genética , Software , Imageamento Tridimensional/métodos , Processamento de Imagem Assistida por Computador/métodos
16.
Sci Data ; 11(1): 722, 2024 Jul 02.
Artigo em Inglês | MEDLINE | ID: mdl-38956115

RESUMO

Around 20% of complete blood count samples necessitate visual review using light microscopes or digital pathology scanners. There is currently no technological alternative to the visual examination of red blood cells (RBCs) morphology/shapes. True/non-artifact teardrop-shaped RBCs and schistocytes/fragmented RBCs are commonly associated with serious medical conditions that could be fatal, increased ovalocytes are associated with almost all types of anemias. 25 distinct blood smears, each from a different patient, were manually prepared, stained, and then sorted into four groups. Each group underwent imaging using different cameras integrated into light microscopes with 40X microscopic lenses resulting in total 47 K + field images/patches. Two hematologists processed cell-by-cell to provide one million + segmented RBCs with their XYWH coordinates and classified 240 K + RBCs into nine shapes. This dataset (Elsafty_RBCs_for_AI) enables the development/testing of deep learning-based (DL) automation of RBCs morphology/shapes examination, including specific normalization of blood smear stains (different from histopathology stains), detection/counting, segmentation, and classification. Two codes are provided (Elsafty_Codes_for_AI), one for semi-automated image processing and another for training/testing of a DL-based image classifier.


Assuntos
Eritrócitos , Eritrócitos/citologia , Humanos , Microscopia , Aprendizado Profundo , Processamento de Imagem Assistida por Computador
17.
Sci Rep ; 14(1): 15219, 2024 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-38956117

RESUMO

Blinding eye diseases are often related to changes in retinal structure, which can be detected by analysing retinal blood vessels in fundus images. However, existing techniques struggle to accurately segment these delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on specific operations can limit its ability to capture crucial details such as the edges of the vessel. This paper introduces LMBiS-Net, a lightweight convolutional neural network designed for the segmentation of retinal vessels. LMBiS-Net achieves exceptional performance with a remarkably low number of learnable parameters (only 0.172 million). The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. In addition, we have optimised the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimisation significantly reduces training time and improves computational efficiency. To assess LMBiS-Net's robustness and ability to generalise to unseen data, we conducted comprehensive evaluations on four publicly available datasets: DRIVE, STARE, CHASE_DB1, and HRF The proposed LMBiS-Net achieves significant performance metrics in various datasets. It obtains sensitivity values of 83.60%, 84.37%, 86.05%, and 83.48%, specificity values of 98.83%, 98.77%, 98.96%, and 98.77%, accuracy (acc) scores of 97.08%, 97.69%, 97.75%, and 96.90%, and AUC values of 98.80%, 98.82%, 98.71%, and 88.77% on the DRIVE, STARE, CHEASE_DB, and HRF datasets, respectively. In addition, it records F1 scores of 83.43%, 84.44%, 83.54%, and 78.73% on the same datasets. Our evaluations demonstrate that LMBiS-Net achieves high segmentation accuracy (acc) while exhibiting both robustness and generalisability across various retinal image datasets. This combination of qualities makes LMBiS-Net a promising tool for various clinical applications.


Assuntos
Aprendizado Profundo , Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Vasos Retinianos , Vasos Retinianos/diagnóstico por imagem , Humanos , Processamento de Imagem Assistida por Computador/métodos , Algoritmos
18.
Sci Rep ; 14(1): 15094, 2024 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-38956139

RESUMO

With the increase in the dependency on digital devices, the incidence of myopia, a precursor of various ocular diseases, has risen significantly. Because myopia and eyeball volume are related, myopia progression can be monitored through eyeball volume estimation. However, existing methods are limited because the eyeball shape is disregarded during estimation. We propose an automated eyeball volume estimation method from computed tomography images that incorporates prior knowledge of the actual eyeball shape. This study involves data preprocessing, image segmentation, and volume estimation steps, which include the truncated cone formula and integral equation. We obtained eyeball image masks using U-Net, HFCN, DeepLab v3 +, SegNet, and HardNet-MSEG. Data from 200 subjects were used for volume estimation, and manually extracted eyeball volumes were used for validation. U-Net outperformed among the segmentation models, and the proposed volume estimation method outperformed comparative methods on all evaluation metrics, with a correlation coefficient of 0.819, mean absolute error of 0.640, and mean squared error of 0.554. The proposed method surpasses existing methods, provides an accurate eyeball volume estimation for monitoring the progression of myopia, and could potentially aid in the diagnosis of ocular diseases. It could be extended to volume estimation of other ocular structures.


Assuntos
Olho , Miopia , Redes Neurais de Computação , Tomografia Computadorizada por Raios X , Humanos , Tomografia Computadorizada por Raios X/métodos , Olho/diagnóstico por imagem , Miopia/diagnóstico por imagem , Miopia/patologia , Feminino , Masculino , Adulto , Processamento de Imagem Assistida por Computador/métodos , Pessoa de Meia-Idade , Adulto Jovem
19.
Sci Rep ; 14(1): 15057, 2024 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-38956224

RESUMO

Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.


Assuntos
Neoplasias Encefálicas , Imageamento por Ressonância Magnética , Redes Neurais de Computação , Imageamento por Ressonância Magnética/métodos , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/patologia , Neoplasias Encefálicas/classificação , Humanos , Processamento de Imagem Assistida por Computador/métodos , Algoritmos , Encéfalo/diagnóstico por imagem , Encéfalo/patologia
20.
Sci Rep ; 14(1): 15507, 2024 Jul 05.
Artigo em Inglês | MEDLINE | ID: mdl-38969713

RESUMO

The mass and volume of Rosa roxburghii fruits are essential for fruit grading and consumer selection. Physical characteristics such as dimension, projected area, mass, and volume are interrelated. Image-based mass and volume estimation facilitates the automation of fruit grading, which can replace time-consuming and laborious manual grading. In this study, image processing techniques were used to extract fruit dimensions and projected areas, and univariate (linear, quadratic, exponential, and power) and multivariate regression models were used to estimate the mass and volume of Rosa roxburghii fruits. The results showed that the quadratic model based on the criterion projected area (CPA) estimated the best mass (R2 = 0.981) with an accuracy of 99.27%, and the equation is M = 0.280 + 0.940CPA + 0.071CPA2. The multivariate regression model based on three projected areas (PA1, PA2, and PA3) estimated the best volume (R2 = 0.898) with an accuracy of 98.24%, and the equation is V = - 8.467 + 0.657PA1 + 1.294PA2 + 0.628PA3. In practical applications, cost savings can be realized by having only one camera position. Therefore, when the required accuracy is low, estimating mass and volume simultaneously from only the dimensional information of the side view or the projected area information of the top view is recommended.


Assuntos
Frutas , Processamento de Imagem Assistida por Computador , Rosa , Processamento de Imagem Assistida por Computador/métodos
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