Journal Of Digital Imaging[JOURNAL]
Sun
200 papers
Multispectral Imaging Method for Rapid Identification and Analysis of Paraffin-Embedded Pathological Tissues.
Sijilmassi O, López Alonso JM, Del Río Sevilla A … +1 more , Barrio Asensio MDC
J Digit Imaging · 2023 Aug · PMID 37072579 · Full text
The study of the interaction between light and biological tissue is of great help in the identification of diseases as well as structural alterations in tissues. In the present study, we have developed a tissue diagnosti...
The study of the interaction between light and biological tissue is of great help in the identification of diseases as well as structural alterations in tissues. In the present study, we have developed a tissue diagnostic technique by using multispectral imaging in the visible spectrum combined with principal component analysis (PCA). We used information from the propagation of light through paraffin-embedded tissues to assess differences in the eye tissues of control mouse embryos compared to mouse embryos whose mothers were deprived of folic acid (FA), a crucial vitamin necessary for the growth and development of the fetus. After acquiring the endmembers from the multispectral images, spectral unmixing was used to identify the abundances of those endmembers in each pixel. For each acquired image, the final analysis was performed by performing a pixel-by-pixel and wavelength-by-wavelength absorbance calculation. Non-negative least squares (NNLS) were used in this research. The abundance maps obtained for the first endmember revealed vascular alterations (vitreous and choroid) in the embryos with maternal FA deficiency. However, the abundance maps obtained for the third endmember showed alterations in the texture of some tissues such as the lens and retina. Results indicated that multispectral imaging applied to paraffin-embedded tissues enhanced tissue visualization. Using this method, first, it can be seen tissue damage location and then decide what kind of biological techniques to apply.
Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT.
Zhong J, Shen H, Chen Y … +10 more , Xia Y, Shi X, Lu W, Li J, Xing Y, Hu Y, Ge X, Ding D, Jiang Z, Yao W
J Digit Imaging · 2023 Aug · PMID 37071291 · Full text
This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V...
This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d'). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d' for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.
Pilot Lightweight Denoising Algorithm for Multiple Sclerosis on Spine MRI.
Mayfield JD, Bailey K, Borkowski AA … +1 more , Viswanadhan N
J Digit Imaging · 2023 Aug · PMID 37069452 · Full text
Multiple sclerosis (MS) is a severely debilitating disease which requires accurate and timely diagnosis. MRI is the primary diagnostic vehicle; however, it is susceptible to noise and artifact which can limit diagnostic...
Multiple sclerosis (MS) is a severely debilitating disease which requires accurate and timely diagnosis. MRI is the primary diagnostic vehicle; however, it is susceptible to noise and artifact which can limit diagnostic accuracy. A myriad of denoising algorithms have been developed over the years for medical imaging yet the models continue to become more complex. We developed a lightweight algorithm which utilizes the image's inherent noise via dictionary learning to improve image quality without high computational complexity or pretraining through a process known as orthogonal matching pursuit (OMP). Our algorithm is compared to existing traditional denoising algorithms to evaluate performance on real noise that would commonly be encountered in a clinical setting. Fifty patients with a history of MS who received 1.5 T MRI of the spine between the years of 2018 and 2022 were retrospectively identified in accordance with local IRB policies. Native resolution 5 mm sagittal images were selected from T2 weighted sequences for evaluation using various denoising techniques including our proposed OMP denoising algorithm. Peak signal to noise ratio (PSNR) and structural similarity index (SSIM) were measured. While wavelet denoising demonstrated an expected higher PSNR than other models, its SSIM was variable and consistently underperformed its comparators (0.94 ± 0.10). Our pilot OMP denoising algorithm provided superior performance with greater consistency in terms of SSIM (0.99 ± 0.01) with similar PSNR to non-local means filtering (NLM), both of which were superior to other comparators (OMP 37.6 ± 2.2, NLM 38.0 ± 1.8). The superior performance of our OMP denoising algorithm in comparison to traditional models is promising for clinical utility. Given its individualized and lightweight approach, implementation into PACS may be more easily incorporated. It is our hope that this technology will provide improved diagnostic accuracy and workflow optimization for Neurologists and Radiologists, as well as improved patient outcomes.
CCS-GAN: COVID-19 CT Scan Generation and Classification with Very Few Positive Training Images.
Menon S, Mangalagiri J, Galita J … +7 more , Morris M, Saboury B, Yesha Y, Yesha Y, Nguyen P, Gangopadhyay A, Chapman D
J Digit Imaging · 2023 Aug · PMID 37069451 · Full text
We present a novel algorithm that is able to generate deep synthetic COVID-19 pneumonia CT scan slices using a very small sample of positive training images in tandem with a larger number of normal images. This generativ...
We present a novel algorithm that is able to generate deep synthetic COVID-19 pneumonia CT scan slices using a very small sample of positive training images in tandem with a larger number of normal images. This generative algorithm produces images of sufficient accuracy to enable a DNN classifier to achieve high classification accuracy using as few as 10 positive training slices (from 10 positive cases), which to the best of our knowledge is one order of magnitude fewer than the next closest published work at the time of writing. Deep learning with extremely small positive training volumes is a very difficult problem and has been an important topic during the COVID-19 pandemic, because for quite some time it was difficult to obtain large volumes of COVID-19-positive images for training. Algorithms that can learn to screen for diseases using few examples are an important area of research. Furthermore, algorithms to produce deep synthetic images with smaller data volumes have the added benefit of reducing the barriers of data sharing between healthcare institutions. We present the cycle-consistent segmentation-generative adversarial network (CCS-GAN). CCS-GAN combines style transfer with pulmonary segmentation and relevant transfer learning from negative images in order to create a larger volume of synthetic positive images for the purposes of improving diagnostic classification performance. The performance of a VGG-19 classifier plus CCS-GAN was trained using a small sample of positive image slices ranging from at most 50 down to as few as 10 COVID-19-positive CT scan images. CCS-GAN achieves high accuracy with few positive images and thereby greatly reduces the barrier of acquiring large training volumes in order to train a diagnostic classifier for COVID-19.
DeepBLS: Deep Feature-Based Broad Learning System for Tissue Phenotyping in Colorectal Cancer WSIs.
Baidar Bakht A, Javed S, Gilani SQ … +3 more , Karki H, Muneeb M, Werghi N
J Digit Imaging · 2023 Aug · PMID 37059892 · Full text
Tissue phenotyping is a fundamental step in computational pathology for the analysis of tumor micro-environment in whole slide images (WSIs). Automatic tissue phenotyping in whole slide images (WSIs) of colorectal cancer...
Tissue phenotyping is a fundamental step in computational pathology for the analysis of tumor micro-environment in whole slide images (WSIs). Automatic tissue phenotyping in whole slide images (WSIs) of colorectal cancer (CRC) assists pathologists in better cancer grading and prognostication. In this paper, we propose a novel algorithm for the identification of distinct tissue components in colon cancer histology images by blending a comprehensive learning system with deep features extraction in the current work. Firstly, we extracted the features from the pre-trained VGG19 network which are then transformed into mapped features space for nodes enhancement generation. Utilizing both mapped features and enhancement nodes, the proposed algorithm classifies seven distinct tissue components including stroma, tumor, complex stroma, necrotic, normal benign, lymphocytes, and smooth muscle. To validate our proposed model, the experiments are performed on two publicly available colorectal cancer histology datasets. We showcase that our approach achieves a remarkable performance boost surpassing existing state-of-the-art methods by (1.3% AvTP, 2% F1) and (7% AvTP, 6% F1) on CRCD-1, and CRCD-2, respectively.
Evaluation of Semiautomatic and Deep Learning-Based Fully Automatic Segmentation Methods on [F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization.
Constantino CS, Leocádio S, Oliveira FPM … +10 more , Silva M, Oliveira C, Castanheira JC, Silva Â, Vaz S, Teixeira R, Neves M, Lúcio P, João C, Costa DC
J Digit Imaging · 2023 Aug · PMID 37059891 · Full text
The objective is to assess the performance of seven semiautomatic and two fully automatic segmentation methods on [F]FDG PET/CT lymphoma images and evaluate their influence on tumor quantification. All lymphoma lesions i...
The objective is to assess the performance of seven semiautomatic and two fully automatic segmentation methods on [F]FDG PET/CT lymphoma images and evaluate their influence on tumor quantification. All lymphoma lesions identified in 65 whole-body [F]FDG PET/CT staging images were segmented by two experienced observers using manual and semiautomatic methods. Semiautomatic segmentation using absolute and relative thresholds, k-means and Bayesian clustering, and a self-adaptive configuration (SAC) of k-means and Bayesian was applied. Three state-of-the-art deep learning-based segmentations methods using a 3D U-Net architecture were also applied. One was semiautomatic and two were fully automatic, of which one is publicly available. Dice coefficient (DC) measured segmentation overlap, considering manual segmentation the ground truth. Lymphoma lesions were characterized by 31 features. Intraclass correlation coefficient (ICC) assessed features agreement between different segmentation methods. Nine hundred twenty [F]FDG-avid lesions were identified. The SAC Bayesian method achieved the highest median intra-observer DC (0.87). Inter-observers' DC was higher for SAC Bayesian than manual segmentation (0.94 vs 0.84, p < 0.001). Semiautomatic deep learning-based median DC was promising (0.83 (Obs1), 0.79 (Obs2)). Threshold-based methods and publicly available 3D U-Net gave poorer results (0.56 ≤ DC ≤ 0.68). Maximum, mean, and peak standardized uptake values, metabolic tumor volume, and total lesion glycolysis showed excellent agreement (ICC ≥ 0.92) between manual and SAC Bayesian segmentation methods. The SAC Bayesian classifier is more reproducible and produces similar lesion features compared to manual segmentation, giving the best concordant results of all other methods. Deep learning-based segmentation can achieve overall good segmentation results but failed in few patients impacting patients' clinical evaluation.
Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study.
Mohebi M, Amini M, Alemzadeh-Ansari MJ … +5 more , Alizadehasl A, Rajabi AB, Shiri I, Zaidi H, Orooji M
J Digit Imaging · 2023 Aug · PMID 37059890 · Full text
In this study, the ability of radiomics features extracted from myocardial perfusion imaging with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post-percutaneous coronary intervention (P...
In this study, the ability of radiomics features extracted from myocardial perfusion imaging with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post-percutaneous coronary intervention (PCI) treatment. A total of 52 patients who had undergone pre-PCI MPI-SPECT were enrolled in this study. After normalization of the images, features were extracted from the left ventricle, initially automatically segmented by k-means and active contour methods, and finally edited and approved by an expert radiologist. More than 1700 2D and 3D radiomics features were extracted from each patient's scan. A cross-combination of three feature selections and seven classifier methods was implemented. Three classes of no or dis-improvement (class 1), improved EF from 0 to 5% (class 2), and improved EF over 5% (class 3) were predicted by using tenfold cross-validation. Lastly, the models were evaluated based on accuracy, AUC, sensitivity, specificity, precision, and F-score. Neighborhood component analysis (NCA) selected the most predictive feature signatures, including Gabor, first-order, and NGTDM features. Among the classifiers, the best performance was achieved by the fine KNN classifier, which yielded mean accuracy, AUC, sensitivity, specificity, precision, and F-score of 0.84, 0.83, 0.75, 0.87, 0.78, and 0.76, respectively, in 100 iterations of classification, within the 52 patients with 10-fold cross-validation. The MPI-SPECT-based radiomic features are well suited for predicting post-revascularization EF and therefore provide a helpful approach for deciding on the most appropriate treatment.
Tensor-RT-Based Transfer Learning Model for Lung Cancer Classification.
Bishnoi V, Goel N
J Digit Imaging · 2023 Aug · PMID 37059889 · Full text
Cancer is a leading cause of death across the globe, in which lung cancer constitutes the maximum mortality rate. Early diagnosis through computed tomography scan imaging helps to identify the stages of lung cancer. Seve...
Cancer is a leading cause of death across the globe, in which lung cancer constitutes the maximum mortality rate. Early diagnosis through computed tomography scan imaging helps to identify the stages of lung cancer. Several deep learning-based classification methods have been employed for developing automatic systems for the diagnosis and detection of computed tomography scan lung slices. However, the diagnosis based on nodule detection is a challenging task as it requires manual annotation of nodule regions. Also, these computer-aided systems have yet not achieved the desired performance in real-time lung cancer classification. In the present paper, a high-speed real-time transfer learning-based framework is proposed for the classification of computed tomography lung cancer slices into benign and malignant. The proposed framework comprises of three modules: (i) pre-processing and segmentation of lung images using K-means clustering based on cosine distance and morphological operations; (ii) tuning and regularization of the proposed model named as weighted VGG deep network (WVDN); (iii) model inference in Nvidia tensor-RT during post-processing for the deployment in real-time applications. In this study, two pre-trained CNN models were experimented and compared with the proposed model. All the models have been trained on 19,419 computed tomography scan lung slices, which were obtained from the publicly available Lung Image Database Consortium and Image Database Resource Initiative dataset. The proposed model achieved the best classification metric, an accuracy of 0.932, precision, recall, an F1 score of 0.93, and Cohen's kappa score of 0.85. A statistical evaluation has also been performed on the classification parameters and achieved a p-value <0.0001 for the proposed model. The quantitative and statistical results validate the improved performance of the proposed model as compared to state-of-the-art methods. The proposed framework is based on complete computed tomography slices rather than the marked annotations and may help in improving clinical diagnosis.
The Keratectasia Volume (KEV) in Corneal Topography to Evaluate the Effect of Corneal Collagen Cross-linking in Pediatric Keratoconus.
Wang X, Zhang B, Li Z … +3 more , Li M, Wang J, Mu G
J Digit Imaging · 2023 Aug · PMID 37059888 · Full text
The study aimed to evaluate the keratectasia volume (KEV) before and after corneal cross-linking (CXL) in pediatric patients. This study included 40 eyes of 25 pediatric patients (10-19 years) undergoing standard CXL. Th...
The study aimed to evaluate the keratectasia volume (KEV) before and after corneal cross-linking (CXL) in pediatric patients. This study included 40 eyes of 25 pediatric patients (10-19 years) undergoing standard CXL. The support vector machine (SVM) algorithm was applied to transform mass pixels in corneal topography into a three-dimensioned model to calculate the KEV. The KEV, Kmax, K1, K2, Kave, keratectasia area (KEA), and thinnest corneal thickness (TCT) were determined before CXL and at 3, 6, and 12 months after surgery. The correlation between KEV and other parameters (Kmax, TCT, max decentration, eccentricity, and so on) was calculated. The KEV was 4.75 ± 0.74 preoperatively and 4.43 ± 1.22 postoperatively at last follow-up (p < 0.002). There was strong positive correlation between the KEV and Kmax (r = 0.806, p < 0.0005). The preoperat ive KEV was 4.32 ± 0.69 in mild to moderate keratoconus (Kmax < 58D) and 5.27 ± 0.37 in advanced keratoconus (Kmax > 58D) (p < 0.0005, t-test). Postoperative KEV and K readings remained stable at the early stage, and the KEV showed a more drastic decreasing trend than Kmax at sixth month. Statistical significance was found in the KEV between preoperative and 6 months after surgery (p < 0.0005), but not in Kmax and other parameters. In 83.3% (15 eyes out of 18 eyes) of the eyes, the preoperative KEV was greater than 4.6 in patients with significant flattening after CXL. Compared with K readings, the KEV can be regarded as a more sensitive index to evaluate the postoperative morphological changes after CXL in pediatric patients.
Comparison Between the Stereoscopic Virtual Reality Display System and Conventional Computed Tomography Workstation in the Diagnosis and Characterization of Cerebral Arteriovenous Malformations.
Liu X, Mao J, Sun N … +8 more , Yu X, Chai L, Tian Y, Wang J, Liang J, Tao H, Wang Z, Lu L
J Digit Imaging · 2023 Aug · PMID 37039950 · Full text
It is difficult to accurately understand the angioarchitecture of cerebral arteriovenous malformations (CAVMs) before surgery using existing imaging methods. This study aimed to evaluate the ability of the stereoscopic v...
It is difficult to accurately understand the angioarchitecture of cerebral arteriovenous malformations (CAVMs) before surgery using existing imaging methods. This study aimed to evaluate the ability of the stereoscopic virtual reality display system (SVRDS) to display the angioarchitecture of CAVMs by comparing its accuracy with that of the conventional computed tomography workstation (CCTW). Nineteen patients with CAVM confirmed on digital subtraction angiography (DSA) or during surgery were studied. Computed tomography angiography images in the SVRDS and CCTW were retrospectively analyzed by two experienced neuroradiologists using a double-blind method. Angioarchitectural parameters, such as the location and size of the nidus, type and number of the arterial feeders and draining veins, and draining pattern of the vessels, were recorded and compared. The diameter of the nidus ranged from 1.1 to 9 cm. Both CCTW and SVRDS correctly diagnosed the location of the nidus in 19 patients with CAVM. Among the 19 patients, 35 arterial feeders and 25 draining veins were confirmed on DSA and during surgery. With the DSA and intraoperative results as the gold standard bases, the CCTW misjudged one arterial feeder and one draining vein and missed three arterial feeders and two draining veins; meanwhile, the SVRDS missed only two arterial feeders. SVRDS had some advantages in displaying nidus, arterial branches, and draining veins of the CAVM compared with CCTW, as well as SVRDS could more intuitively display the overall angio-architectural spatial picture of CAVM.
RTFusion: A Multimodal Fusion Network with Significant Information Enhancement.
Fan C, Chen Z, Wang X … +2 more , Xuan Z, Zhu Z
J Digit Imaging · 2023 Aug · PMID 37038040 · Full text
Multimodal medical fusion images are important for clinical diagnosis because they can better reflect the location of disease and provide anatomically detailed information. Existing medical image fusion methods can cause...
Multimodal medical fusion images are important for clinical diagnosis because they can better reflect the location of disease and provide anatomically detailed information. Existing medical image fusion methods can cause significant information loss in fusion images to varying degrees. Therefore, we designed a residual transformer fusion network (RTFusion): a multimodal fusion network with significant information enhancement. We use the residual transformer to make the image information interact remotely to ensure the global information of the image and use the residual structure to enhance the feature information to prevent information loss. Then the channel attention and spatial attention module (CASAM) is added to the fusion process to enhance the significant information of the fusion image, and the feature interaction module is used to promote the interaction of specific information of the source image. Finally, the loss function of the block calculation is designed to drive the fusion network to retain rich texture details, structural information, and color information, to optimize the subjective visual effect of the image. Extensive experiments show that our method can better recover the significant information of the source image and outperform other advanced methods in subjective visual description and objective metric evaluation. In particular, the color information and texture information are balanced to enhance the visual effect of the fused image.
Lossy Image Compression in a Preclinical Multimodal Imaging Study.
Cunha FF, Blüml V, Zopf LM … +7 more , Walter A, Wagner M, Weninger WJ, Thomaz LA, Tavora LMN, da Silva Cruz LA, Faria SMM
J Digit Imaging · 2023 Aug · PMID 37038039 · Full text
The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context,...
The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compression in pre-clinical contexts as a potential alleviation of this problem. In this work, the authors study the application of lossy image coding to compress high-resolution volumetric biomedical data. The impact of compression on the metrics and interpretation of volumetric data was quantified for a correlated multimodal imaging study to characterize murine tumor vasculature, using volumetric high-resolution episcopic microscopy (HREM), micro-computed tomography (µCT), and micro-magnetic resonance imaging (µMRI). The effects of compression were assessed by measuring task-specific performances of several biomedical experts who interpreted and labeled multiple data volumes compressed at different degrees. We defined trade-offs between data volume reduction and preservation of visual information, which ensured the preservation of relevant vasculature morphology at maximum compression efficiency across scales. Using the Jaccard Index (JI) and the average Hausdorff Distance (HD) after vasculature segmentation, we could demonstrate that, in this study, compression that yields to a 256-fold reduction of the data size allowed to keep the error induced by compression below the inter-observer variability, with minimal impact on the assessment of the tumor vasculature across scales.
Artificial Intelligence-driven Digital Cytology-based Cervical Cancer Screening: Is the Time Ripe to Adopt This Disruptive Technology in Resource-constrained Settings? A Literature Review.
Gupta R, Kumar N, Bansal S … +3 more , Singh S, Sood N, Gupta S
J Digit Imaging · 2023 Aug · PMID 37029285 · Full text
Cervical cancer is still a public health scourge in the developing countries due to the lack of organized screening programs. Though liquid-based cytology methods improved the performance of cervical cytology, the interp...
Cervical cancer is still a public health scourge in the developing countries due to the lack of organized screening programs. Though liquid-based cytology methods improved the performance of cervical cytology, the interpretation still suffers from subjectivity. Artificial intelligence (AI) algorithms have offered objectivity leading to better sensitivity and specificity of cervical cancer screening. Whole slide imaging (WSI) that converts a glass slide to a virtual slide provides a new perspective to the application of AI, especially for cervical cytology. In the recent years, there have been a few studies employing various AI algorithms on WSI images of conventional or LBC smears and demonstrating differing sensitivity/specificity or accuracy at detection of abnormalities in cervical smears. Considering the interest in AI-based screening modalities, this well-timed review intends to summarize the progress in this field while highlighting the research gaps and providing future research directions.
Skin Lesion Segmentation in Dermoscopic Images with Noisy Data.
Lama N, Hagerty J, Nambisan A … +2 more , Stanley RJ, Van Stoecker W
J Digit Imaging · 2023 Aug · PMID 37020149 · Full text
We propose a deep learning approach to segment the skin lesion in dermoscopic images. The proposed network architecture uses a pretrained EfficientNet model in the encoder and squeeze-and-excitation residual structures i...
We propose a deep learning approach to segment the skin lesion in dermoscopic images. The proposed network architecture uses a pretrained EfficientNet model in the encoder and squeeze-and-excitation residual structures in the decoder. We applied this approach on the publicly available International Skin Imaging Collaboration (ISIC) 2017 Challenge skin lesion segmentation dataset. This benchmark dataset has been widely used in previous studies. We observed many inaccurate or noisy ground truth labels. To reduce noisy data, we manually sorted all ground truth labels into three categories - good, mildly noisy, and noisy labels. Furthermore, we investigated the effect of such noisy labels in training and test sets. Our test results show that the proposed method achieved Jaccard scores of 0.807 on the official ISIC 2017 test set and 0.832 on the curated ISIC 2017 test set, exhibiting better performance than previously reported methods. Furthermore, the experimental results showed that the noisy labels in the training set did not lower the segmentation performance. However, the noisy labels in the test set adversely affected the evaluation scores. We recommend that the noisy labels should be avoided in the test set in future studies for accurate evaluation of the segmentation algorithms.
Glomerulus Detection Using Segmentation Neural Networks.
Singh Samant S, Chauhan A, Dn J … +1 more , Singh V
J Digit Imaging · 2023 Aug · PMID 37020148 · Full text
Digital pathology is vital for the correct diagnosis of kidney before transplantation or kidney disease identification. One of the key challenges in kidney diagnosis is glomerulus detection in kidney tissue segments. In...
Digital pathology is vital for the correct diagnosis of kidney before transplantation or kidney disease identification. One of the key challenges in kidney diagnosis is glomerulus detection in kidney tissue segments. In this study, we propose a deep learning-based method for glomerulus detection from digitized kidney slide segments. The proposed method applies models based on convolutional neural networks to detect image segments containing the glomerulus region. We employ various networks such as ResNets, UNet, LinkNet, and EfficientNet to train the models. In our experiments on a network trained on the NIH HuBMAP kidney whole slide image dataset, the proposed method achieves the highest scores with Dice coefficient of 0.942.
A Structure-Aware Convolutional Neural Network for Automatic Diagnosis of Fungal Keratitis with In Vivo Confocal Microscopy Images.
Liang S, Zhong J, Zeng H … +4 more , Zhong P, Li S, Liu H, Yuan J
J Digit Imaging · 2023 Aug · PMID 37014469 · Full text
Fungal keratitis (FK) is a common and severe corneal disease, which is widely spread in tropical and subtropical areas. Early diagnosis and treatment are vital for patients, with confocal microscopy cornea imaging being...
Fungal keratitis (FK) is a common and severe corneal disease, which is widely spread in tropical and subtropical areas. Early diagnosis and treatment are vital for patients, with confocal microscopy cornea imaging being one of the most effective methods for the diagnosis of FK. However, most cases are currently diagnosed by the subjective judgment of ophthalmologists, which is time-consuming and heavily depends on the experience of the ophthalmologists. In this paper, we introduce a novel structure-aware automatic diagnosis algorithm based on deep convolutional neural networks for the accurate diagnosis of FK. Specifically, a two-stream convolutional network is deployed, combining GoogLeNet and VGGNet, which are two commonly used networks in computer vision architectures. The main stream is used for feature extraction of the input image, while the auxiliary stream is used for feature discrimination and enhancement of the hyphae structure. Then, the features are combined by concatenating the channel dimension to obtain the final output, i.e., normal or abnormal. The results showed that the proposed method achieved accuracy, sensitivity, and specificity of 97.73%, 97.02%, and 98.54%, respectively. These results suggest that the proposed neural network could be a promising computer-aided FK diagnosis solution.
Influence of Data Augmentation Strategies on the Segmentation of Oral Histological Images Using Fully Convolutional Neural Networks.
Dos Santos DFD, de Faria PR, Travençolo BAN … +1 more , do Nascimento MZ
J Digit Imaging · 2023 Aug · PMID 37012446 · Full text
Segmentation of tumor regions in H &E-stained slides is an important task for a pathologist while diagnosing different types of cancer, including oral squamous cell carcinoma (OSCC). Histological image segmentation is of...
Segmentation of tumor regions in H &E-stained slides is an important task for a pathologist while diagnosing different types of cancer, including oral squamous cell carcinoma (OSCC). Histological image segmentation is often constrained by the availability of labeled training data since labeling histological images is a highly skilled, complex, and time-consuming task. Thus, data augmentation strategies become essential to train convolutional neural networks models to overcome the overfitting problem when only a few training samples are available. This paper proposes a new data augmentation strategy, named Random Composition Augmentation (RCAug), to train fully convolutional networks (FCN) to segment OSCC tumor regions in H &E-stained histological images. Given the input image and their corresponding label, a pipeline with a random composition of geometric, distortion, color transfer, and generative image transformations is executed on the fly. Experimental evaluations were performed using an FCN-based method to segment OSCC regions through a set of different data augmentation transformations. By using RCAug, we improved the FCN-based segmentation method from 0.51 to 0.81 of intersection-over-union (IOU) in a whole slide image dataset and from 0.65 to 0.69 of IOU in a tissue microarray images dataset.
Enhancing Multi-disease Diagnosis of Chest X-rays with Advanced Deep-learning Networks in Real-world Data.
Chen Y, Wan Y, Pan F
J Digit Imaging · 2023 Aug · PMID 36988837 · Full text
The current artificial intelligence (AI) models are still insufficient in multi-disease diagnosis for real-world data, which always present a long-tail distribution. To tackle this issue, a long-tail public dataset, "Che...
The current artificial intelligence (AI) models are still insufficient in multi-disease diagnosis for real-world data, which always present a long-tail distribution. To tackle this issue, a long-tail public dataset, "ChestX-ray14," which involved fourteen (14) disease labels, was randomly divided into the train, validation, and test sets with ratios of 0.7, 0.1, and 0.2. Two pretrained state-of-the-art networks, EfficientNet-b5 and CoAtNet-0-rw, were chosen as the backbones. After the fully-connected layer, a final layer of 14 sigmoid activation units was added to output each disease's diagnosis. To achieve better adaptive learning, a novel loss (L) was designed, which coalesced reweighting and tail sample focus. For comparison, a pretrained ResNet50 network with weighted binary cross-entropy loss (L) was used as a baseline, which showed the best performance in a previous study. The overall and individual areas under the receiver operating curve (AUROC) for each disease label were evaluated and compared among different models. Group-score-weighted class activation mapping (Group-CAM) is applied for visual interpretations. As a result, the pretrained CoAtNet-0-rw + L showed the best overall AUROC of 0.842, significantly higher than ResNet50 + L (AUROC: 0.811, p = 0.037). Group-CAM presented that the model could pay the proper attention to lesions for most disease labels (e.g., atelectasis, edema, effusion) but wrong attention for the other labels, such as pneumothorax; meanwhile, mislabeling of the dataset was found. Overall, this study presented an advanced AI diagnostic model achieving a significant improvement in the multi-disease diagnosis of chest X-rays, particularly in real-world data with challenging long-tail distributions.
Employing Multiple Low-Dose PET Images (at Different Dose Levels) as Prior Knowledge to Predict Standard-Dose PET Images.
Sanaei B, Faghihi R, Arabi H
J Digit Imaging · 2023 Aug · PMID 36988836 · Full text
The existing deep learning-based denoising methods predicting standard-dose PET images (S-PET) from the low-dose versions (L-PET) solely rely on a single-dose level of PET images as the input of deep learning network. In...
The existing deep learning-based denoising methods predicting standard-dose PET images (S-PET) from the low-dose versions (L-PET) solely rely on a single-dose level of PET images as the input of deep learning network. In this work, we exploited the prior knowledge in the form of multiple low-dose levels of PET images to estimate the S-PET images. To this end, a high-resolution ResNet architecture was utilized to predict S-PET images from 6 to 4% L-PET images. For the 6% L-PET imaging, two models were developed; the first and second models were trained using a single input of 6% L-PET and three inputs of 6%, 4%, and 2% L-PET as input to predict S-PET images, respectively. Similarly, for 4% L-PET imaging, a model was trained using a single input of 4% low-dose data, and a three-channel model was developed getting 4%, 3%, and 2% L-PET images. The performance of the four models was evaluated using structural similarity index (SSI), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE) within the entire head regions and malignant lesions. The 4% multi-input model led to improved SSI and PSNR and a significant decrease in RMSE by 22.22% and 25.42% within the entire head region and malignant lesions, respectively. Furthermore, the 4% multi-input network remarkably decreased the lesions' SUV bias and SUV bias by 64.58% and 37.12% comparing to single-input network. In addition, the 6% multi-input network decreased the RMSE within the entire head region, within the lesions, lesions' SUV bias, and SUV bias by 37.5%, 39.58%, 86.99%, and 45.60%, respectively. This study demonstrated the significant benefits of using prior knowledge in the form of multiple L-PET images to predict S-PET images.
Diabetic Retinopathy Prediction Based on Wavelet Decomposition and Modified Capsule Network.
Oulhadj M, Riffi J, Khodriss C … +7 more , Mahraz AM, Bennis A, Yahyaouy A, Chraibi F, Abdellaoui M, Andaloussi IB, Tairi H
J Digit Imaging · 2023 Aug · PMID 36973632 · Full text
Diabetic retinopathy (DR) is one of the most common consequences of diabetes. It affects the retina, causing blood vessel damage which can lead to loss of vision. Saving patients from losing their sight or at least slowi...
Diabetic retinopathy (DR) is one of the most common consequences of diabetes. It affects the retina, causing blood vessel damage which can lead to loss of vision. Saving patients from losing their sight or at least slowing the progress of this disease depends mainly on the early detection of this pathology, on top of the detection of its specific stage. Furthermore, the early detection of diabetic retinopathy and the follow-up of the patient's condition remains an arduous task, whether for an experienced expert ophthalmologist or a computer-aided diagnosis technician. In this paper, we aim to propose a new automatic diabetic retinopathy severity level detection method. The proposed approach merges the pyramid hierarchy of the discrete wavelet transform of the retina fundus image with the modified capsule network and the modified inception block proposed, in addition to a new deep hybrid model that concatenates the inception block with capsule networks. The performance of our proposed approach has been validated on the APTOS dataset, as it achieved a high training accuracy of 97.71% and a high testing accuracy score of 86.54%, which is considered one of the best scores achieved in this field using the same dataset.
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