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2.
Sensors (Basel) ; 22(1)2022 Jan 02.
Artigo em Inglês | MEDLINE | ID: mdl-35009871

RESUMO

Recently, many super-resolution reconstruction (SR) feedforward networks based on deep learning have been proposed. These networks enable the reconstructed images to achieve convincing results. However, due to a large amount of computation and parameters, SR technology is greatly limited in devices with limited computing power. To trade-off the network performance and network parameters. In this paper, we propose the efficient image super-resolution network via Self-Calibrated Feature Fuse, named SCFFN, by constructing the self-calibrated feature fuse block (SCFFB). Specifically, to recover the high-frequency detail information of the image as much as possible, we propose SCFFB by self-transformation and self-fusion of features. In addition, to accelerate the network training while reducing the computational complexity of the network, we employ an attention mechanism to elaborate the reconstruction part of the network, called U-SCA. Compared with the existing transposed convolution, it can greatly reduce the computation burden of the network without reducing the reconstruction effect. We have conducted full quantitative and qualitative experiments on public datasets, and the experimental results show that the network achieves comparable performance to other networks, while we only need fewer parameters and computational resources.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética
3.
J Synchrotron Radiat ; 29(Pt 1): 239-246, 2022 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-34985441

RESUMO

Rodents are used extensively as animal models for the preclinical investigation of microvascular-related diseases. However, motion artifacts in currently available imaging methods preclude real-time observation of microvessels in vivo. In this paper, a pixel temporal averaging (PTA) method that enables real-time imaging of microvessels in the mouse brain in vivo is described. Experiments using live mice demonstrated that PTA efficiently eliminated motion artifacts and random noise, resulting in significant improvements in contrast-to-noise ratio. The time needed for image reconstruction using PTA with a normal computer was 250 ms, highlighting the capability of the PTA method for real-time angiography. In addition, experiments with less than one-quarter of photon flux in conventional angiography verified that motion artifacts and random noise were suppressed and microvessels were successfully identified using PTA, whereas conventional temporal subtraction and averaging methods were ineffective. Experiments performed with an X-ray tube verified that the PTA method could also be successfully applied to microvessel imaging of the mouse brain using a laboratory X-ray source. In conclusion, the proposed PTA method may facilitate the real-time investigation of cerebral microvascular-related diseases using small animal models.


Assuntos
Artefatos , Processamento de Imagem Assistida por Computador , Animais , Camundongos , Microvasos/diagnóstico por imagem , Radiografia , Raios X
4.
J Synchrotron Radiat ; 29(Pt 1): 266-275, 2022 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-34985444

RESUMO

A transmission X-ray microscope (TXM) can investigate morphological and chemical information of a tens to hundred micrometre-thick specimen on a length scale of tens to hundreds of nanometres. It has broad applications in material sciences and battery research. TXM data processing is composed of multiple steps. A workflow software has been developed that integrates all the tools required for general TXM data processing and visualization. The software is written in Python and has a graphic user interface in Jupyter Notebook. Users have access to the intermediate analysis results within Jupyter Notebook and have options to insert extra data processing steps in addition to those that are integrated in the software. The software seamlessly integrates ImageJ as its primary image viewer, providing rich image visualization and processing routines. As a guide for users, several TXM specific data analysis issues and examples are also presented.


Assuntos
Análise de Dados , Microscopia , Processamento de Imagem Assistida por Computador , Software , Fluxo de Trabalho , Raios X
5.
Cancer Imaging ; 22(1): 8, 2022 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-35033188

RESUMO

BACKGROUND : Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN). METHODS: The dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively. RESULTS: The average manual segmented primary tumor volume was 11.8±6.70 cm3 with a median [IQR] of 13.9 [3.22-15.9] cm3. The tumor volume measured by MV-CNN was 22.8±21.1 cm3 with a median [IQR] of 16.0 [8.24-31.1] cm3. Compared to the manual segmentations, the MV-CNN scored an average ICC of 0.64±0.06 and a DSC of 0.49±0.19. Improved segmentation performance was observed with increasing primary tumor volume: the smallest tumor volume group (<3 cm3) scored a DSC of 0.26±0.16 and the largest group (>15 cm3) a DSC of 0.63±0.11 (p<0.001). The automated segmentation tended to overestimate compared to the manual reference, both around the actual primary tumor and in false positively classified healthy structures and pathologically enlarged lymph nodes. CONCLUSION: An automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in treatment planning.


Assuntos
Neoplasias de Cabeça e Pescoço , Imageamento por Ressonância Magnética , Neoplasias de Cabeça e Pescoço/diagnóstico por imagem , Humanos , Processamento de Imagem Assistida por Computador , Carga Tumoral
6.
Artigo em Japonês | MEDLINE | ID: mdl-35046218

RESUMO

PURPOSE: To test whether deep learning can be used to effectively reduce artifacts in MR images of the brain. METHODS: In this study, a large set of images with and without motion artifacts is needed for training. It is difficult to collect training data from clinical images because it requires a lot of effort and time. We have created motion artifact images of the brain by computer simulation. As an experimental study, we obtained original images for deep learning from 20 volunteers. These original images were used to create various images of different artifacts by computer simulation and these were used the input images for deep learning. The same method was used to create test images and these images were used to compare the structural similarity (SSIM) index and peak signal-to-noise ratio (PSNR) between the input images and output images using the three denoising methods. The network models used were U-shaped fully convolutional network (U-Net), denoising convolutional neural network (DnCNN) and wide inference network and 5 layers Residual learning and batch normalization (Win5RB). RESULTS: U-Net was the most effective model for reducing motion artifacts. The SSIM and PSNR were 0.978 and 32.5 dB. CONCLUSION: This is an effective method to reduce artifacts without degrading the image quality of brain MRI images.


Assuntos
Aprendizado Profundo , Treinamento por Simulação , Artefatos , Encéfalo/diagnóstico por imagem , Simulação por Computador , Humanos , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Razão Sinal-Ruído
7.
Comput Intell Neurosci ; 2022: 2105790, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35047031

RESUMO

With the continuous development of social economy, sports have received more and more attention. How to improve the quality of sports has become the focus of research. The computer digital 3D video image processing is introduced in this paper, taking shooting as the starting point, in which computer digitization technology is used to collect images of sequence targets through combining the operation flow of shooting, monitor the results and data of shooting and process 3D video images, conduct the analyze and mine according to the corresponding statistical processing results, and evaluate the corresponding training. The simulation experiment proves that the computerized digital 3D video image processing is effective and can scientifically support sports-assisted training.


Assuntos
Processamento de Imagem Assistida por Computador , Esportes , Computadores , Imageamento Tridimensional , Gravação em Vídeo
8.
Sichuan Da Xue Xue Bao Yi Xue Ban ; 53(1): 114-120, 2022 Jan.
Artigo em Chinês | MEDLINE | ID: mdl-35048610

RESUMO

Objective: To examine the performance and application value of improved Unet network technology in the recognition and segmentation of hemorrhage regions in brain CT images. Methods: A total of 476 brain CT images of patients with spontaneous intracerebral hemorrhage (SICH) were retrospectively included. The improved Unet network was used to identify and segment the hemorrhage regions in the patients' brain CT images. The CT imaging data of the hemorrhage regions were manually labelled by clinicians. After randomized sorting, 430 data sets from 106 patients were selected for inclusion in the training set and 46 data sets from 11 patients were included in the test set. After data enhancement, the experimental data set underwent network training and model testing in order to assess the segmentation performance. The segmentation results were compared with the those of the Unet network (Base), FCN-8s network and Unet++ network. Results: In the segmentation of brain CT image hemorrhage region with the improved Unet network, the three evaluation indicators of Dice similarity coefficient, positive predictive value (PPV), and sensitivity coefficient (SC) reached 0.8738, 0.9011 and 0.8648, respectively, increasing by 8.80%, 7.14% and 8.96%, respectively, compared with those of FCN-8s, and increasing by 4.56%, 4.44% and 4.15%, respectively, compared with those of Unet network (Base). The improved Unet network also showed better segmentation performance than that of Unet++ network. Conclusion: The improved method based on Unet network proposed in this report displayed good performance in the recognition and segmentation of hemorrhage regions in brain CT images, and is an appropriate method for the recognition and segmentation of hemorrhage regions in brain CT images, showing potential application value for assisting clinical decision-making and preventing early hematoma expansion.


Assuntos
Processamento de Imagem Assistida por Computador , Tomografia Computadorizada por Raios X , Encéfalo/diagnóstico por imagem , Hemorragia , Humanos , Estudos Retrospectivos
9.
Anal Chim Acta ; 1191: 339308, 2022 Jan 25.
Artigo em Inglês | MEDLINE | ID: mdl-35033246

RESUMO

An artificial intelligence approach based on deep generative neural networks for spectral imaging processing was proposed. The key idea was to treat different spectral image processing operations such as segmentation, regression, and classification as image-to-image translation tasks. For the image-to-image translation, the conditional generative adversarial networks were used. As a baseline comparison, the traditional chemometric approach based on pixels wise modelling was demonstrated. The analysis was presented with two real data sets related to fruit property prediction and kernel and shell classification of walnuts. The presented artificial intelligence approach for spectral image processing can provide benefits for any field of science where spectral imaging and processing is widely performed.


Assuntos
Inteligência Artificial , Redes Neurais de Computação , Frutas , Processamento de Imagem Assistida por Computador
10.
BMC Bioinformatics ; 23(1): 46, 2022 Jan 18.
Artigo em Inglês | MEDLINE | ID: mdl-35042474

RESUMO

BACKGROUND: Algorithmic cellular segmentation is an essential step for the quantitative analysis of highly multiplexed tissue images. Current segmentation pipelines often require manual dataset annotation and additional training, significant parameter tuning, or a sophisticated understanding of programming to adapt the software to the researcher's need. Here, we present CellSeg, an open-source, pre-trained nucleus segmentation and signal quantification software based on the Mask region-convolutional neural network (R-CNN) architecture. CellSeg is accessible to users with a wide range of programming skills. RESULTS: CellSeg performs at the level of top segmentation algorithms in the 2018 Kaggle Data Challenge both qualitatively and quantitatively and generalizes well to a diverse set of multiplexed imaged cancer tissues compared to established state-of-the-art segmentation algorithms. Automated segmentation post-processing steps in the CellSeg pipeline improve the resolution of immune cell populations for downstream single-cell analysis. Finally, an application of CellSeg to a highly multiplexed colorectal cancer dataset acquired on the CO-Detection by indEXing (CODEX) platform demonstrates that CellSeg can be integrated into a multiplexed tissue imaging pipeline and lead to accurate identification of validated cell populations. CONCLUSION: CellSeg is a robust cell segmentation software for analyzing highly multiplexed tissue images, accessible to biology researchers of any programming skill level.


Assuntos
Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Algoritmos , Fluorescência , Software
11.
Gen Dent ; 70(1): 51-55, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34978991

RESUMO

The objectives of the present study were to compare measurements of pharyngeal airway subregions on lateral cephalometric (LC) and cone beam computed tomographic (CBCT) images in relation to skeletal classes and discuss the advantages and disadvantages of these imaging formats for this type of assessment. The CBCT images were assessed via both multiplanar reconstruction (MPR) and 3-dimensional (3D) reconstruction. The LC and CBCT images from 107 patients were classified according to skeletal class: I, n = 35; II, n = 35; and III, n = 37. Linear measurements of the subdivisions of the upper airway were performed on the LC, MPR, and 3D images. In addition, area and volumetric measurements were performed on the MPR images. The relationships among imaging methods, skeletal class, and pharyngeal thirds were assessed by means of a 1-way analysis of variance (α = 0.05). No statistically significant differences in the linear, area, or volumetric measurements of the upper airway subregions were found among the skeletal classes (P > 0.05). For the linear measurements in the oropharynx and hypopharynx, greater values were observed for the LC images than for the MPR and 3D images (P ≤ 0.05). Based on the study findings, MPR images should be preferred for visualization of the pharyngeal airway subregions. However, LC imaging is preferable to 3D reconstruction.


Assuntos
Tomografia Computadorizada de Feixe Cônico , Processamento de Imagem Assistida por Computador , Cefalometria , Humanos , Imageamento Tridimensional , Orofaringe/diagnóstico por imagem , Faringe/diagnóstico por imagem
12.
Med Biol Eng Comput ; 60(2): 487-500, 2022 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-35015271

RESUMO

An important step in brain image analysis is to divide specific brain regions by matching brain slices to standard brain reference atlases, and perform statistical analysis on the labeled neurons in each brain region. Taking mouse fluorescently labeled brain slices as an example, due to the noise and distortion introduced during the preparation of brain slices, and the modal differences with standard brain atlas, the brain slices cannot directly establish an accurate one-to-one correspondence with the brain atlas, which in turn affects the accuracy of the number of labeled neurons in each brain region. This paper introduces the idea of image representation, uses neural networks to realize the registration of different modal mouse brain slices and brain atlas, completes the regional localization of the brain slices, and uses threshold segmentation to detect and count the labeled neurons in each brain region. The method proposed in this paper can effectively solve the problem of large deviation of neurons count caused by the inaccurate division of brain regions in large deformed brain slices, and can automatically realize accurate count of labeled neurons in each brain region of brain slices. The whole framework of method for counting labeled neurons in mouse brain regions based on image representation and registration.


Assuntos
Encéfalo , Processamento de Imagem Assistida por Computador , Animais , Encéfalo/diagnóstico por imagem , Cabeça , Imageamento por Ressonância Magnética , Camundongos , Redes Neurais de Computação , Neurônios
14.
Meat Sci ; 183: 108654, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34419789

RESUMO

In the European Community, conformation and fat cover of bovine carcasses is assessed using the SEUROP grading system. In this study we pursued the development of an application software (App) based on Visual Image Analysis, useful for SEUROP and Fat Cover grading of bovine carcasses using a smartphone. The App was trained using 500 bovine carcasses. Carcass conformation and Fat Cover classes were assessed in parallel by expert evaluators and by App. Overall, a high correspondence was found between the measurements of carcasses parameters by operators and by the App, as high as 84.2% for SEUROP and 86.4% for the Fat Cover. In the 15.8% of samples with discordant SEUROP evaluation, and in the 13.6% of samples with discordant Fat Cover evaluation, the operators' and App measurements deviated by only one class. All values also aligned with the requirements expected by the current legislation for the use of automated and/or semi-automated systems able to determine the market value of carcasses.


Assuntos
Tecido Adiposo/anatomia & histologia , Processamento de Imagem Assistida por Computador/métodos , Carne Vermelha/análise , Animais , Composição Corporal , Bovinos , União Europeia , Carne Vermelha/normas
15.
Meat Sci ; 184: 108671, 2022 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-34656003

RESUMO

Deep Learning (DL) has proven to be a successful tool for many image classification problems but has yet to be applied to carcass images. The aim of this study was to train DL models to predict carcass cut yields and compare predictions to more standard machine learning (ML) methods. Three approaches were undertaken to predict the grouped carcass cut yields of Grilling cuts and Roasting cuts from a large dataset of 54,598 and 69,246 animals respectively. The approaches taken were (1) animal phenotypic data used as features for a range of ML algorithms, (2) carcass images used to train Convolutional Neural Networks, and (3) carcass dimensions measured directly from the carcass images, combined with the associated phenotypic data and used as feature data for ML algorithms. Results showed that DL models can be trained to predict carcass cuts yields but an approach that uses carcass dimensions in ML algorithms performs slightly better in absolute terms.


Assuntos
Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Carne Vermelha/classificação , Animais , Composição Corporal , Bovinos , Aprendizado de Máquina
16.
Br J Radiol ; 95(1129): 20210759, 2022 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-34889645

RESUMO

OBJECTIVE: To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcription polymerase chain reaction (RT-PCR) test. METHODS: In this retrospective analysis, data of COVID-19 suspected patients who underwent RT-PCR and chest CT examination for the diagnosis of COVID-19 were assessed. By quantifying the affected area of the lung parenchyma, severity score was evaluated for each lobe of the lung with the DL-based algorithm. The diagnosis was based on the total lung severity score ranging from 0 to 25. The data were randomly split into a 40% training set and a 60% test set. Optimal cut-off value was determined using Youden-index method on the training cohort. RESULTS: A total of 1259 patients were enrolled in this study. The prevalence of RT-PCR positivity in the overall investigated period was 51.5%. As compared to RT-PCR, sensitivity, specificity, positive predictive value, negative predictive value and accuracy on the test cohort were 39.0%, 80.2%, 68.0%, 55.0% and 58.9%, respectively. Regarding the whole data set, when adding those with positive RT-PCR test at any time during hospital stay or "COVID-19 without virus detection", as final diagnosis to the true positive cases, specificity increased from 80.3% to 88.1% and the positive predictive value increased from 68.4% to 81.7%. CONCLUSION: DL-based CT severity score was found to have a good specificity and positive predictive value, as compared to RT-PCR. This standardized scoring system can aid rapid diagnosis and clinical decision making. ADVANCES IN KNOWLEDGE: DL-based CT severity score can detect COVID-19-related lung alterations even at early stages, when RT-PCR is not yet positive.


Assuntos
COVID-19/diagnóstico por imagem , Aprendizado Profundo , Adulto , Idoso , COVID-19/diagnóstico , COVID-19/patologia , Reações Falso-Negativas , Reações Falso-Positivas , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Masculino , Radiografia Torácica , Reprodutibilidade dos Testes , Estudos Retrospectivos , Sensibilidade e Especificidade , Índice de Gravidade de Doença , Tomografia Computadorizada por Raios X
17.
IEEE Trans Pattern Anal Mach Intell ; 44(1): 100-113, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-32750803

RESUMO

Exploiting multi-scale representations is critical to improve edge detection for objects at different scales. To extract edges at dramatically different scales, we propose a bi-directional cascade network (BDCN) architecture, where an individual layer is supervised by labeled edges at its specific scale, rather than directly applying the same supervision to different layers. Furthermore, to enrich multi-scale representations learned by each layer of BDCN, we introduce a scale enhancement module (SEM), which utilizes dilated convolution to generate multi-scale features, instead of using deeper CNNs. These new approaches encourage the learning of multi-scale representations in different layers and detect edges that are well delineated by their scales. Learning scale dedicated layers also results in a compact network with a fraction of parameters. We evaluate our method on three datasets, i.e., BSDS500, NYUDv2, and Multicue, and achieve ODS F-measure of 0.832, 2.7 percent higher than current state-of-the-art on the BSDS500 dataset. We also applied our edge detection result to other vision tasks. Experimental results show that, our method further boosts the performance of image segmentation, optical flow estimation, and object proposal generation.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador , Aprendizagem
18.
Methods Mol Biol ; 2368: 95-109, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34647251

RESUMO

Root gravitropic bending is a complex growth process resulting from differential expansion of cells on the upper and lower sides of a gravistimulated root. In order to genetically dissect the molecular machinery underlying root bending, a thorough understanding of the kinetics and spatial distribution of the growth process is required. We have developed an experimental workflow that enables us to image growing roots at high spatiotemporal resolution and then convert XY-coordinates of root cellular markers into 3D representations of root growth profiles. Here, we present a detailed description of the setup for monitoring vertically oriented roots before and after gravistimulation. We also introduce our newly developed custom R-based program RootPlot, which calculates root velocity profiles from root XY-coordinate data obtained using a previously published image processing software. The raw velocity and derived relative elemental growth rate (REGR) curves are then fitted via LOWESS regression for assumption-free data analysis. The resulting smoothed growth profiles are plotted as heatmaps to visualize how different regions of the root contribute to the growth response over time. Additionally, RootPlot provides analysis of overall growth and bending rates based on root XY-coordinates.


Assuntos
Fenômenos Biomecânicos , Gravitropismo , Raízes de Plantas , Processamento de Imagem Assistida por Computador , Software
19.
Eur Radiol ; 32(1): 517-523, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34132877

RESUMO

PURPOSE: This study evaluates the performance of a mobile and compact hybrid C-arm scanner (referred to as IXSI) that is capable of simultaneous acquisition of 2D fluoroscopic and nuclear projections and 3D image reconstruction in the intervention room. RESULTS: The impact of slightly misaligning the IXSI modalities (in an off-focus geometry) was investigated for the reduction of the fluoroscopic and nuclear interference. The 2D and 3D nuclear image quality of IXSI was compared with a clinical SPECT/CT scanner by determining the spatial resolution and sensitivity of point sources and by performing a quantitative analysis of the reconstructed NEMA image quality phantom. The 2D and 3D fluoroscopic image of IXSI was compared with a clinical CBCT scanner by visualizing the Fluorad A+D image quality phantom and by visualizing a reconstructed liver nodule phantom. Finally, the feasibility of dynamic simultaneous nuclear and fluoroscopic imaging was demonstrated by injecting an anthropomorphic phantom with a mixture of iodinated contrast and 99mTc. CONCLUSION: Due to the divergent innovative hybrid design of IXSI, concessions were made to the nuclear and fluoroscopic image qualities. Nevertheless, IXSI realizes unique image guidance that may be beneficial for several types of procedures. KEY POINTS: • IXSI can perform time-resolved planar (2D) simultaneous fluoroscopic and nuclear imaging. • IXSI can perform SPECT/CBCT imaging (3D) inside the intervention room.


Assuntos
Imageamento Tridimensional , Tomografia Computadorizada de Emissão de Fóton Único , Tomografia Computadorizada de Feixe Cônico , Fluoroscopia , Humanos , Processamento de Imagem Assistida por Computador , Imagens de Fantasmas
20.
Anticancer Res ; 42(1): 419-427, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34969752

RESUMO

BACKGROUND/AIM: With the progress in cancer immunotherapy using immune checkpoint blockade (ICB) therapy, histological observations of tumor-infiltrating lymphocyte (TIL) status are needed to evaluate the antitumor effect of ICB using imaging analysis software. MATERIALS AND METHODS: Formalin-fixed paraffin-embedded sections obtained from colorectal cancer and gastric cancer patients with more than 500 single nucleotide variants were stained with anti-CD8 and anti-PD-1 antibodies. Based on our own algorithm and imaging analysis software, an automatic TIL measurement method was established and compared to the manual counting methods. RESULTS: In the CD8+ T cell number measurement, there was a good correlation (r=0.738 by Pearson test) between the manual and automated counting methods. However, in the PD-1+ T cell measurement, there was a large difference in TIL numbers in both groups. After adjustment of the parameter settings, the correlation between the manual and automated methods in the PD-1+ T cell measurements improved (r=0.668 by Pearson test). CONCLUSION: An imaging software-based automatic measurement could be a simple and useful tool for evaluating the therapeutic effect of cancer immunotherapies in terms of TIL status.


Assuntos
Antígenos CD8/genética , Neoplasias Colorretais/genética , Receptor de Morte Celular Programada 1/genética , Neoplasias Gástricas/genética , Anticorpos Anti-Idiotípicos/imunologia , Anticorpos Anti-Idiotípicos/farmacologia , Antígenos CD8/isolamento & purificação , Linfócitos T CD8-Positivos/metabolismo , Linfócitos T CD8-Positivos/patologia , Neoplasias Colorretais/imunologia , Neoplasias Colorretais/patologia , Neoplasias Colorretais/terapia , Humanos , Processamento de Imagem Assistida por Computador , Inibidores de Checkpoint Imunológico/uso terapêutico , Linfócitos do Interstício Tumoral/patologia , Masculino , Polimorfismo de Nucleotídeo Único/genética , Receptor de Morte Celular Programada 1/isolamento & purificação , Software , Neoplasias Gástricas/imunologia , Neoplasias Gástricas/patologia , Neoplasias Gástricas/terapia
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