Pseudo-color Images Research Articles

Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour

This study addresses computer-aided breast cancer diagnosis through a hybrid framework for breast tumor segmentation in ultrasound images. The core of the three-stage method is based on the autoencoder convolutional neural network. In the first stage, we prepare a hybrid pseudo-color image through multiple instances of fuzzy connectedness analysis with a novel distance-adapted fuzzy affinity. We produce different weight combinations to determine connectivity maps driven by particular image specifics. After the hybrid image is processed by the deep network, we adjust the segmentation outcome with the Chan-Vese active contour model. We find the idea of incorporating fuzzy connectedness into the input data preparation for deep-learning image analysis our main contribution to the study. The method is trained and validated using a combined dataset of 993 breast ultrasound images from three public collections frequently used in recent studies on breast tumor segmentation. The experiments address essential settings and hyperparameters of the method, e.g., the network architecture, input image size, and active contour setup. The tumor segmentation reaches a median Dice index of 0.86 (mean at 0.79) over the combined database. We refer our results to the most recent state-of-the-art from 2022–2023 using the same datasets, finding our model comparable in segmentation performance.

Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.

The purpose of this study is to investigate the impact of using morphological information in classifying suspicious breast lesions. The widespread use of deep transfer learning can significantly improve the performance of themammogram based CADx schemes.However, digital mammograms are grayscale images, while deep learning models are typically optimized using the natural images containing three channels. Thus, it is needed to convert the grayscale mammograms into three channel images for the input of deep transfer models. This study aims to developa novel pseudo color image generation methodwhich utilizesthe mass contour information to enhance the classification performance.Accordingly, a total of 830 breast cancer cases were retrospectively collected, which contains 310 benign and 520 malignant cases, respectively. For each case, a total of four regions of interest (ROI) are collected from the grayscale images captured for both the CC and MLO views of the two breasts. Meanwhile, a total of seven pseudo color image sets are generated as the input of the deep learning models, which arecreated through a combination of the original grayscale image, a histogram equalized image, a bilaterally filtered image, and a segmented mass. Accordingly, the output features from four identical pre-trained deep learning models are concatenated and then processed by a support vector machine-based classifier to generate the final benign/malignant labels. The performance of each image set was evaluated and compared. The results demonstrate that the pseudo color sets containing the manually segmented mass performed significantly better than all other pseudo color sets, which achieved an AUC (area under the ROC curve) up to 0.889 ± 0.012 and an overall accuracy up to 0.816 ± 0.020, respectively. At the same time, the performance improvement is also dependent on the accuracy of the mass segmentation. The results of this study support our hypothesis that adding accurately segmented mass contours can provide complementary information, thereby enhancing the performance of the deep transfer model in classifying suspicious breast lesions.

Feasibility and potential of terahertz spectral and imaging technology for Apple Valsa canker detection: A preliminary investigation

Apple Valsa canker (AVC) caused by the Ascomycete Valsa mali, seriously constrains the production and quality of apple fruits. The symptomless incubation characteristics of Valsa mali make it highly challenging to detect AVC at an early infection stage. After infecting the wound of apple bark, the pathogenic hyphae of AVC will expand and colonize the phloem tissue. Meanwhile, various enzymes and toxic substances released by hyphae cause the decomposition of cellulose and lignin, and the generation of poisonous secondary metabolites in bark tissue. However, these early symptoms of AVC are invisible from the bark’s appearance. Fortunately, Terahertz Spectral Imaging (ThzSI) technology with the advantage of penetrating, and fingerprinting is promising for detecting hidden or slight symptoms of the fungal infection. This study is a preliminary investigation of terahertz frequency-domain spectra for AVC in the early stage of infection. Healthy and two-week-infected apple tree branches were prepared for capturing ThzS images, and the spectral data were preprocessed by Multivariate scattering correction (MSC), Savitzky-Golay convolution smoothing (SG), and standard normal variate (SNV) respectively to remove data noise and improve data quality. Principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and random frog (RFROG) were employed to extract the spectral feature bands to eliminate redundant data and improve computational efficiency. Machine learning models were established based on the spectral features to detect AVC at an early infection stage, where 11 of them exhibited the best performance with F1-score of 99.72%. To further explore disease information in spatial spectra, imaging data were acquired using terahertz imaging technology. Based on imaging data, pseudo-color imaging, histogram equalization, and Otsu segmentation were employed to visualize early infection areas in apple barks. Furthermore, histogram feature (HF), shape feature (SF), and local binary pattern (LBP) extracted from terahertz spectral images were utilized to establish the SVM, RF, and KNN models. HF-SF-KNN and HF-SF-LBP-KNN with the best performance achieved F1-score of 98.82%. This study presents a preliminary application of terahertz spectral and imaging technology for early-stage AVC detection and demonstrates its feasibility. Additionally, it provides a new way to detect AVC, which expands the application of ThzSI technology in tree disease detection in orchards and lays the foundation for further research.

Distinguishing of Histopathological Staging Features of H-E Stained Human cSCC by Microscopical Multispectral Imaging.

Cutaneous squamous cell carcinoma (cSCC) is the second most common malignant skin tumor. Early and precise diagnosis of tumor staging is crucial for long-term outcomes. While pathological diagnosis has traditionally served as the gold standard, the assessment of differentiation levels heavily depends on subjective judgments. Therefore, how to improve the diagnosis accuracy and objectivity of pathologists has become an urgent problem to be solved. We used multispectral imaging (MSI) to enhance tumor classification. The hematoxylin and eosin (H&E) stained cSCC slides were from Shanghai Ruijin Hospital. Scale-invariant feature transform was applied to multispectral images for image stitching, while the adaptive threshold segmentation method and random forest segmentation method were used for image segmentation, respectively. Synthetic pseudo-color images effectively highlight tissue differences. Quantitative analysis confirms significant variation in the nuclear area between normal and cSCC tissues (p < 0.001), supported by an AUC of 1 in ROC analysis. The AUC within cSCC tissues is 0.57. Further study shows higher nuclear atypia in poorly differentiated cSCC tissues compared to well-differentiated cSCC (p < 0.001), also with an AUC of 1. Lastly, well differentiated cSCC tissues show more and larger keratin pearls. These results have shown that combined MSI with imaging processing techniques will improve H&E stained human cSCC diagnosis accuracy, and it will be well utilized to distinguish histopathological staging features.

Research on denoising method based on temperature and humidity profile lidar

Due to the fact that the vibration and pure rotational Raman signals collected by the temperature and humidity profile lidar were 3–4 orders of magnitude weaker than the Mie scattering signal, they were susceptible to electronic and white noise interference, which seriously affected the system signal-to-noise ratio. In this paper, an improved VMD-WT filtering method was adopted to extract effective signals and denoise. The processing outcome of several filtering algorithms was evaluated, and noisy signals were simulated to confirm the algorithm's efficacy. Based on the quantitative computation of evaluation indicators, such as signal-to-noise ratio, root mean square error, and correlation, the improved VMD-WT algorithm had more significant advantages in indicators such as signal-to-noise ratio. In order to further verify the robustness and adaptability of the proposed algorithm, experimental analysis of the filtering algorithm was conducted on the continuously collected temperature and humidity measured signals. The results demonstrated that the algorithm not only improved the detection range of lidar and suppressed high-altitude noise effectively, but also performed well in processing strong interference signals, like clouds, which led to a significant improvement in the atmospheric optical parameter inversion results. Furthermore, pseudo-color images of aerosols, temperature, and humidity changes over time and space have been used to further illustrate the algorithm's dependability and wide range of potential uses.

A quantitative approach to reflectance transformation imaging in profilometric applications

Reflectance transformation imaging (RTI) is a technique employed to assess both the intensity and directional properties of light reflected from an object, aiming to visualize an object under different incident light directions. This technique has quickly become a commonly used approach for the documentation, acquisition and deciphering for cultural heritage objects, because it enables to enhance and highlight image details. The output of this mathematical synthesis technique, which elaborates illumination information, is contained in a pseudocolour image called normal map. Starting from this image, this technique allows to obtain the reconstruction of the outline of semi-flat objects by integrating the normal map given by the RTI, i.e. normal integration. The technique of normal integration has been known for many years, being widely used in graphical modelling across various fields of 3D imaging. In the context of cultural heritage, this particular form of 3D modelling has already found application in the reconstruction of semi-flat objects, albeit with some limitations in the accurate portraying of low spatial frequencies. However, quantitative and systematic studies on the efficiency of 3D rendering of objects using RTI and normal integration have not yet been conducted. In the context of this research, the quantitative evaluation of the accuracy of the RTI technique with normal integration for profilometric applications on semi-flat objects is proposed and a study on its fidelity in reproducing the considered artefact is conducted. For this purpose, ad hoc profilometric targets were designed, realized and analysed to quantitatively compare the results of RTI and normal integration with different standardized techniques: micro-photogrammetry, laser scanning and optical profilometry.Graphical abstract

Adaptive pseudo-colour image enhancement algorithm for high-grey X-ray film based on pixel self-transformation

Adaptive pseudo-colour image enhancement algorithm for high-grey X-ray film based on pixel self-transformation

Interreader and Intermodality Variability in Macular Atrophy Quantification in Neovascular Age-related Macular Degeneration: Comparison of 6 Imaging Modalities

Macular atrophy is a common complication in neovascular age-related macular degeneration (AMD) and is associated with poorer visual outcomes. This study evaluated interreader and intermodality variability in measuring macular atrophy in previously treated neovascular AMD eyes without exudation using 6 imaging modalities. Prospective, cohort study. Thirty participants with previously treated neovascular AMD showing no signs of exudation at the time of enrollment and exhibiting macular atrophy. During the same clinic visit, patients were imaged using 6 different imaging modalities: color fundus photography (CFP; Clarus, Carl Zeiss Meditec), near-infrared imaging (NIR; Spectralis, Heidelberg Engineering), structural OCT (Spectralis, Heidelberg Engineering), green fundus autofluorescence (GAF; Clarus, Carl Zeiss Meditec), blue fundus autofluorescence (BAF; Spectralis, Heidelberg Engineering), and pseudocolor imaging (MultiColor; Spectralis, Heidelberg Engineering). Two readers independently measured the macular atrophy area. Interreader and intermodality agreement. The 95% coefficient of repeatability was 5.98 mm2 for CFP, 4.46 mm2 for MultiColor, 3.90 mm2 for BAF, 3.92 mm2 for GAF, 4.86 mm2 for NIR, and 3.55 mm2 for OCT. Similarly, the coefficient of variation was lowest for OCT-based grading at 0.08 and highest for NIR-based grading at 0.28. Accordingly, the intraclass correlation coefficient was 0.742 for CFP, 0.805 for MultiColor, 0.857 for BAF, 0.850 for GAF, 0.755 for NIR, and 0.917 for OCT. The 6 different imaging modalities presented measurements with different mean values, with only a limited number of comparisons not significantly different between the instruments, although measurements were correlated. The largest size of macular atrophy was measured with structural OCT-based grading (median= 4.65 mm2; interquartile range [IQR]= 4.78 mm2) and the smallest was with CFP-based grading (median= 3.86 mm2; IQR= 5.06 mm2). Inconsistencies arose from various factors. In patients with neovascular AMD, macular atrophy measurements vary significantly depending on the imaging technique used. Color fundus photography-based assessments yielded the smallest macular atrophy sizes, whereas structural OCT-based assessments produced the largest. These discrepancies stem from both the inherent limitations of each modality in assessing retinal pigment epithelial atrophy and factors related to neovascularization, such as the coexistence of fibrosis. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Influence of Image Factors on the Performance of Ophthalmic Ultrasound Deep Learning Model

ObjectiveThis study aims to evaluate the impact of image factors on the performance of deep learning models used for ophthalmic ultrasound image diagnosis. MethodsA total of 3,373 ophthalmic ultrasound images are used to deeply evaluate the influence of image factors on the performance of deep learning classification models. Inceptionv3, Xception, and the fusion model Inceptionv3-Xception are used to explore how brightness, contrast, gain, noise, size, format, pseudo-color seven image-related factors affect the classification performance of the model. ResultsInceptionv3-Xception has advantages in the recognition accuracy of various image factors. When the image brightness changes, the model's performance shows a downward trend (0.5 vs. 1 vs. 1.8, ACC 95.73 vs. 97.06 vs. 93.54, P < 0.05). When the image contrast changes, the model's performance is comparable (0.5 vs. 1 vs. 1.2, ACC 96.23 vs. 96.95 vs. 97.45, P > 0.05). When the image gain drops to 50 dB, the model's accuracy decreases significantly (50 dB vs. 105 dB, ACC 96.49 vs. 97.57, P < 0.05). When Gaussian noise is added to the image, the model's performance gradually decreases (0.02 vs. 0, ACC 89.48vs97.06, P < 0.05). When the image size drops to 25% of the original image, the model's performance decreases significantly (25% vs. 100%, ACC 93.18 vs. 97.06, P < 0.01). When the image format changes, the model's recognition accuracy is similar (JPG vs. BMP vs. PNG, ACC 96.98 vs. 97.06 vs. 97.06, P > 0.05). The accuracy of the model in recognizing pseudo-color images decreases significantly compared to grayscale images (grayscale vs. pseudo-color, ACC 35.96 vs. 97.06). ConclusionThese results indicate that image quality greatly influences the model training process, and acquiring high-quality images is an important prerequisite for high recognition performance of the model. This study offers valuable insights for the improvement of other robust deep learning models for ophthalmic ultrasound image recognition.

Contraband detection method in X-ray image based on improved CenterNet network

Security inspection has been played a crucial role in ensuring the safety of railway passenger transportation. The detection of contraband in X-ray images is an important part of safety inspection work. Since the X-ray image of security inspection is a pseudo color image with a unique imaging style, and the restricted objects are mostly small targets, the accuracy of the object detection algorithm using visible light images directly is not high. This research will propose an improved CenterNet method, and design a 16 times down sampling rate backbone network, which is more suitable for detecting contraband in X-ray images. This article will introduce attention mechanisms to increase attention to effective and key areas in X-ray images, and reveal the mechanisms of spatial and channel attention in X-ray images. Furthermore, by designing an adaptive feature fusion mechanism, the drawback of the original CenterNet by using only the last layer of output features is compensated. Experiments have shown that on the SIXray dataset, mAP is improved by 3.3% compared to the original CenterNet method, and 5.5%, 2.4%, 3.6%, 10.1% compared to SSD, Yolo V3, FPN, DERT algorithms. The detection accuracy of restricted items in X-ray images is effectively improved.

Automatic identification of cirques based on RetinaNet model and pseudo-color image fusion method

Cirque landforms, which are distinctive features of cold regions, play a pivotal role in understanding environmental changes and climate research. Currently, machine learning-based automatic glacier recognition models have significantly improved recognition speed. However, these models rely solely on elevation data from Digital Elevation Model (DEM), neglecting other topographic factors that influence cirque identification, such as slope and aspect. This study integrates DEM, slope, and aspect data through pseudo-color fusion technology and utilizes the RetinaNet network to achieve automatic cirque recognition. The pseudo-color fusion image-based recognition model outperforms methods solely relying on DEM, achieving mAP (Intersection over Union: IoU @0.5) of 82.3 %. In practical applications, the pseudo-color fusion image-based recognition model demonstrates balanced performance in identifying different categories of cirques, particularly excelling in recognizing intricate terrains and irregularly shaped cirques.

Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network

The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles, and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles. By fusing band combination optimization with deep learning, this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line. By applying hyperspectral imaging and a one-dimensional deep learning algorithm, we detect and classify impurities in seed cotton after harvest. The main categories detected include pure cotton, conveyor belt, film covering seed cotton, and film adhered to the conveyor belt. The proposed method achieves an impurity detection rate of 99.698%. To further ensure the feasibility and practical application potential of this strategy, we compare our results against existing mainstream methods. In addition, the model shows excellent recognition performance on pseudo-color images of real samples. With a processing time of 11.764 μs per pixel from experimental data, it shows a much improved speed requirement while maintaining the accuracy of real production lines. This strategy provides an accurate and efficient method for removing impurities during cotton processing.

A painting authentication method based on multi-scale spatial-spectral feature fusion and convolutional neural network

The scientific authentication of paintings holds significant importance within the realm of art collection. Employing convolutional neural networks for the classification of authentic and counterfeit painting images based on color images is a viable but suboptimal choice. This study investigates the potential for authenticating paintings by incorporating high-spectral images alongside high-resolution spatial images. High-resolution and high-spectral images of genuine and counterfeit paintings were acquired using a push-broom digital scanning system. The processing methods presented in this approach for the acquired images are: 1) The study utilized the circular local binary pattern (LBP) and principal component analysis (PCA) to extract surface texture and spectral data from Chinese character images in paintings, encompassing both spatial and spectral dimensions. 2) The technique utilizing non-subsampling Shearlet transform (NSST) and pulse-coupled neural network (PCNN) was employed to integrate the spatial and spectral characteristics of the images into a pseudo-color image, producing a dataset of feature data for genuine and counterfeit paintings. 3) The experiments aimed to achieve the authenticity of artworks using EfficientNet v2-s, the hyperparameters of which were fine-tuned accordingly. The experimental findings demonstrate that this approach attained a 90.8 % accuracy on the test dataset, representing a 3.5 % enhancement over the existing top-performing three-dimensional convolutional neural network (3D-CNN).

Upconversion-based hydrogel kit with Python-assisted analysis platform for sample-to-result detection of organophosphorus pesticide

On-site quantitative analysis of pesticide residues is crucial for monitoring environmental quality and ensuring food safety. Herein, we have developed a reliable hydrogel portable kit using NaYbF4@NaYF4: Yb, Tm upconversion nanoparticles (UCNPs) combined with MnO2 nanoflakes. This portable kit is integrated with a smartphone reader and Python-assisted analysis platform to enable sample-to-result analysis for chlorpyrifos. The novel UCNPs maximizes energy donation to MnO2 acceptor by employing 100 % of activator Yb3+ in the nucleus for NIR excitation energy collection and confining emitter Tm3+ to the surface layer to shorten energy transfer distance. Under NIR excitation, efficient quenching of upconversion blue-violet emission by MnO2 nanoflakes occurs, and the quenched emission is recovered with acetylcholinesterase-mediated reactions. This process allows for the determination of chlorpyrifos by inhibiting enzymatic activity. The UCNPs/MnO2 were embedded to fabricate a hydrogel portable kit, the blue-violet emission images captured by smartphone were converted into corresponding gray values by Python-assisted superiority chart algorithm which achieves a real-time rapid quantitative analysis of chlorpyrifos with a detection limit of 0.17 ng mL−1. At the same time, pseudo-color images were also added by Python in “one run” to distinguish images clearly. This sensor detection with Python-assisted analysis platform provides a new perspective on pesticide monitoring and broadens the application prospects in bioanalysis.

Relationship between macular perfusion and lesion distribution in diabetic retinopathy

Background/objectivesTo assess the relationship between macular vessel density metrics and foveal avascular zone (FAZ) characteristics on optical coherence tomography angiography (OCTA) and lesion distribution in eyes with diabetic retinopathy (DR).Subjects/methodsPatients with DR who underwent both Optos ultrawidefield (UWF) pseudocolor imaging and macular OCTA (Cirrus Angioplex, 6 × 6 mm) were included in this cross-sectional observational study. The distribution of DR lesions was assessed by comparing each of the peripheral ETDRS extended fields (3–7) against their corresponding ETDRS field, hence eyes were defined as either having predominantly peripheral lesions (PPL) or predominantly central lesions (PCL). En face OCTA images from the superficial and deep capillary plexuses (SCP and DCP) were then analysed using Image J software. Perfusion density (PD), vessel length density (VLD), and fractal dimensions (FD) were calculated following binarization and skeletonization of the images.ResultsOut of 344 eyes, 116 (33.72%) eyes had PPL and 228 (66.28%) eyes had PCL. For all DRSS levels, VLD, PD, and FD were not significantly different between eyes with PPL and PCL. The FAZ in eyes with PPL, however, was found to be more circular in shape compared to eyes with PCL (p = 0.037).ConclusionAlthough the presence of PPL has been associated with a higher risk for diabetic retinopathy progression, the macular perfusion is similar in eyes with PPL and PCL. The FAZ is more circular in eyes with PPL, but the clinical relevance of this difference remains to be defined.

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis: A Cross-Sectional Multicenter Study

In ophthalmology, the quality of fundus images is critical for accurate diagnosis, both in clinical practice and in artificial intelligence (AI)-assisted diagnostics. Despite the broad view provided by ultrawide-field (UWF) imaging, pseudocolor images may conceal critical lesions necessary for precise diagnosis. To address this, we introduce UWF-Net, a sophisticated image enhancement algorithm that takes disease characteristics into consideration. Using the Fudan University ultra-wide-field image (FDUWI) dataset, which includes 11 294 Optos pseudocolor and 2415 Zeiss true-color UWF images, each of which is rigorously annotated, UWF-Net combines global style modeling with feature-level lesion enhancement. Pathological consistency loss is also applied to maintain fundus feature integrity, significantly improving image quality. Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization (CLAHE) and structure and illumination constrained generative adversarial network (StillGAN), delivering superior retinal image quality, higher quality scores, and preserved feature details after enhancement. In disease classification tasks, images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN, demonstrating a 4.62% increase in sensitivity (SEN) and a 3.97% increase in accuracy (ACC). In a multicenter clinical setting, UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors, and yielded a significant reduction in diagnostic time ((13.17 ± 8.40) s for UWF-Net enhanced images vs (19.54 ± 12.40) s for original images) and an increase in diagnostic accuracy (87.71% for UWF-Net enhanced images vs 80.40% for original images). Our research verifies that UWF-Net markedly improves the quality of UWF imaging, facilitating better clinical outcomes and more reliable AI-assisted disease classification. The clinical integration of UWF-Net holds great promise for enhancing diagnostic processes and patient care in ophthalmology.

Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra-wide field imaging.

To elaborate a deep learning (DL) model for automatic prediction of late recurrence (LR) of rhegmatogenous retinal detachment (RRD) using pseudocolor and fundus autofluorescence (AF) ultra-wide field (UWF) images obtained preoperatively and postoperatively. We retrospectively included patients >18 years who underwent either scleral buckling (SB) or pars plana vitrectomy (PPV) for primary or recurrent RRD with a post-operative follow-up >2 years. Records of RRD recurrence between 6 weeks and 2 years after surgery served as a ground truth for the training of the deep learning (DL) models. Four separate DL models were trained to predict LR within the 2 postoperative years (binary outputs) using, respectively, UWF preoperative and postoperative pseudocolor images and UWF preoperative and postoperative AF images. A total of 412 eyes were included in the study (332 eyes treated with PPV and 80 eyes with SB). The mean follow-up was 4.0 ± 2.1 years. The DL models based on preoperative and postoperative pseudocolor UWF imaging predicted recurrence with 85.6% (sensitivity 86.7%, specificity 85.4%) and 90.2% accuracy (sensitivity 87.0%, specificity 90.8%) in PPV-treated eyes, and 87.0% (sensitivity 86.7%, specificity 87.0%) and 91.1% (sensitivity 88.2%, specificity 91.9%) in SB-treated eyes, respectively. The DL models using preoperative and postoperative AF-UWF imaging predicted recurrence with 87.6% (sensitivity 84.0% and specificity 88.3%) and 91.0% (sensitivity 88.9%, specificity 91.5%) accuracy in PPV eyes, and 86.5% (sensitivity 87.5%; specificity 86.2%) and 90.6% (sensitivity 90.0%, specificity 90.7%) in SB eyes, respectively. Among the risk factors detected with visualisation methods, potential novel ones were extensive laser retinopexy and asymmetric staphyloma. DL can accurately predict the LR of RRD based on UWF images (especially postoperative ones), which can help refine follow-up strategies. Saliency maps might provide further insight into the dynamics of RRD recurrence.

Multi-modal medical image fusion using improved dual-channel PCNN.

This paper proposes a medical image fusion method in the non-subsampled shearlet transform (NSST) domain to combine a gray-scale image with the respective pseudo-color image obtained through different imaging modalities. The proposed method applies a novel improved dual-channel pulse-coupled neural network (IDPCNN) model to fuse the high-pass sub-images, whereas the Prewitt operator is combined with maximum regional energy (MRE) to construct the fused low-pass sub-image. First, the gray-scale image and luminance of the pseudo-color image are decomposed using NSST to find the respective sub-images. Second, the low-pass sub-images are fused by the Prewitt operator and MRE-based rule. Third, the proposed IDPCNN is utilized to get the fused high-pass sub-images from the respective high-pass sub-images. Fourth, the luminance of the fused image is obtained by applying inverse NSST on the fused sub-images, which is combined with the chrominance components of the pseudo-color image to construct the fused image. A total of 28 diverse medical image pairs, 11 existing methods, and nine objective metrics are used in the experiment. Qualitative and quantitative fusion results show that the proposed method is competitive with and even outpaces some of the existing medical fusion approaches. It is also shown that the proposed method efficiently combines two gray-scale images.

Photobleaching Mimics Retinal Lesions in Pseudocolor Imaging.

Photobleaching Mimics Retinal Lesions in Pseudocolor Imaging.