In Vivo Imaging of Retinal and Choroidal Morphology and Vascular Plexuses of Vertebrates Using Swept-Source Optical Coherence Tomography

Multifractal Analysis of Choroidal SDOCT Images in the Detection of Retinitis Pigmentosa

The aim of this paper is to investigate whether a multifractal analysis can be applied to study choroidal blood vessels and help ophthalmologists in the early diagnosis of retinitis pigmentosa (RP). In a case study, we used spectral domain optical coherence tomography (SDOCT), which is a noninvasive and highly sensitive imaging technique of the retina and choroid. The image of a choroidal branching pattern can be regarded as a multifractal. Therefore, we calculated the generalized Renyi point-centered dimensions, which are considered a measure of the inhomogeneity of data, to prove that it increases in patients with RP as compared to those in the control group.

The crucial role of gadolinium-enhanced MRI in a case of amaurosis fugax - a case report and literature review

Optic perineuritis is the inflammation of the optic nerve sheath. This affliction can lead to visual field impairment and other signs and symptoms related to the orbital space, such as pain, disc edema, ophthalmoplegia, proptosis. However, not all patients present with such suggestive symptoms, requiring a thorough assessment. We report the case of a young male admitted to our hospital for recurrent episodes of monocular blindness. Amaurosis fugax is a well-known presentation of transient ischemic attacks (TIA) and it was ruled out. Gadolinium-enhanced MRI revealed a typical aspect of optic perineuritis. It was mandatory to consider all possible causes of secondary optic perineuritis as they all represent serious clinical conditions, even if the idiopathic form is more frequent. The clinical and paraclinical evaluation of the patient excluded an underlying disease and primary optic perineuritis was diagnosed. Corticosteroid therapy is usually curative and a course of methylprednisolone was initiated for our patient with good outcome. However, response to treatment is not diagnostic as both primary and secondary optic perineuritis are normally responsive, hence thorough differential diagnosis is necessary.

Distinctive approach in brain tumor detection and feature extraction using biologically inspired DWT method and SVM

Brain tumors result from uncontrolled cell growth, potentially leading to fatal consequences if left untreated. While significant efforts have been made with some promising results, the segmentation and classification of brain tumors remain challenging due to their diverse locations, shapes, and sizes. In this study, we employ a combination of Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) to enhance performance and streamline the medical image segmentation process. Proposed method using Otsu's segmentation method followed by PCA to identify the most informative features. Leveraging the grey-level co-occurrence matrix, we extract numerous valuable texture features. Subsequently, we apply a Support Vector Machine (SVM) with various kernels for classification. We evaluate the proposed method's performance using metrics such as accuracy, sensitivity, specificity, and the Dice Similarity Index coefficient. The experimental results validate the effectiveness of our approach, with recall rates of 86.9%, precision of 95.2%, F-measure of 90.9%, and overall accuracy. Simulation of the results shows improvements in both quality and accuracy compared to existing techniques. In results section, experimental Dice Similarity Index coefficient of 0.82 indicates a strong overlap between the machine-extracted tumor region and the manually delineated tumor region.

MRChexNet: Multi-modal bridge and relational learning for thoracic disease recognition in chest X-rays

While diagnosing multiple lesion regions in chest X-ray (CXR) images, radiologists usually apply pathological relationships in medicine before making decisions. Therefore, a comprehensive analysis of labeling relationships in different data modes is essential to improve the recognition performance of the model. However, most automated CXR diagnostic methods that consider pathological relationships treat different data modalities as independent learning objects, ignoring the alignment of pathological relationships among different data modalities. In addition, some methods that use undirected graphs to model pathological relationships ignore the directed information, making it difficult to model all pathological relationships accurately. In this paper, we propose a novel multi-label CXR classification model called MRChexNet that consists of three modules: a representation learning module (RLM), a multi-modal bridge module (MBM) and a pathology graph learning module (PGL). RLM captures specific pathological features at the image level. MBM performs cross-modal alignment of pathology relationships in different data modalities. PGL models directed relationships between disease occurrences as directed graphs. Finally, the designed graph learning block in PGL performs the integrated learning of pathology relationships in different data modalities. We evaluated MRChexNet on two large-scale CXR datasets (ChestX-Ray14 and CheXpert) and achieved state-of-the-art performance. The mean area under the curve (AUC) scores for the 14 pathologies were 0.8503 (ChestX-Ray14) and 0.8649 (CheXpert). MRChexNet effectively aligns pathology relationships in different modalities and learns more detailed correlations between pathologies. It demonstrates high accuracy and generalization compared to competing approaches. MRChexNet can contribute to thoracic disease recognition in CXR.

A multi-band centroid contrastive reconstruction fusion network for motor imagery electroencephalogram signal decoding

Motor imagery (MI) brain-computer interface (BCI) assist users in establishing direct communication between their brain and external devices by decoding the movement intention of human electroencephalogram (EEG) signals. However, cerebral cortical potentials are highly rhythmic and sub-band features, different experimental situations and subjects have different categories of semantic information in specific sample target spaces. Feature fusion can lead to more discriminative features, but simple fusion of features from different embedding spaces leading to the model global loss is not easily convergent and ignores the complementarity of features. Considering the similarity and category contribution of different sub-band features, we propose a multi-band centroid contrastive reconstruction fusion network (MB-CCRF). We obtain multi-band spatio-temporal features by frequency division, preserving the task-related rhythmic features of different EEG signals; use a multi-stream cross-layer connected convolutional network to perform a deep feature representation for each sub-band separately; propose a centroid contrastive reconstruction fusion module, which maps different sub-band and category features into the same shared embedding space by comparing with category prototypes, reconstructing the feature semantic structure to ensure that the global loss of the fused features converges more easily. Finally, we use a learning mechanism to model the similarity between channel features and use it as the weight of fused sub-band features, thus enhancing the more discriminative features, suppressing the useless features. The experimental accuracy is 79.96% in the BCI competition Ⅳ-Ⅱa dataset. Moreover, the classification effect of sub-band features of different subjects is verified by comparison tests, the category propensity of different sub-band features is verified by confusion matrix tests and the distribution in different classes of each sub-band feature and fused feature are showed by visual analysis, revealing the importance of different sub-band features for the EEG-based MI classification task.

Aberrant intrinsic functional brain topology in methamphetamine-dependent individuals after six-months of abstinence

Our aim was to explore the aberrant intrinsic functional topology in methamphetamine-dependent individuals after six months of abstinence using resting-state functional magnetic imaging (rs-fMRI). Eleven methamphetamines (MA) abstainers who have abstained for six months and eleven healthy controls (HC) were recruited for rs-fMRI examination. The graph theory and functional connectivity (FC) analysis were employed to investigate the aberrant intrinsic functional brain topology between the two groups at multiple levels. Compared with the HC group, the characteristic shortest path length ($ {L}_{p} $) showed a significant decrease at the global level, while the global efficiency ($ {E}_{glob} $) and local efficiency ($ {E}_{loc} $) showed an increase considerably. After FDR correction, we found significant group differences in nodal degree and nodal efficiency at the regional level in the ventral attentional network (VAN), dorsal attentional network (DAN), somatosensory network (SMN), visual network (VN) and default mode network (DMN). In addition, the NBS method presented the aberrations in edge-based FC, including frontoparietal network (FPN), subcortical network (SCN), VAN, DAN, SMN, VN and DMN. Moreover, the FC of large-scale functional brain networks revealed a decrease within the VN and SCN and between the networks. These findings suggest that some functions, e.g., visual processing skills, object recognition and memory, may not fully recover after six months of withdrawal. This leads to the possibility of relapse behavior when confronted with MA-related cues, which may contribute to explaining the relapse mechanism. We also provide an imaging basis for revealing the neural mechanism of MA-dependency after six months of abstinence.

Multi-omics analysis and validation of the tumor microenvironment of hepatocellular carcinoma under RNA modification patterns

BACKGROUND

Multiple types of RNA modifications are associated with the prognosis of hepatocellular carcinoma (HCC) patients. However, the overall mediating effect of RNA modifications on the tumor microenvironment (TME) and the prognosis of patients with HCC is unclear.

METHODS

Thoroughly analyze the TME, biological processes, immune infiltration and patient prognosis based on RNA modification patterns and gene patterns. Construct a prognostic model (RNA modification score, RNAM-S) to predict the overall survival (OS) in HCC patients. Analyze the immune status, cancer stem cell (CSC), mutations and drug sensitivity of HCC patients in both the high and low RNAM-S groups. Verify the expression levels of the four characteristic genes of the prognostic RNAM-S using in vitro cell experiments.

RESULTS

Two modification patterns and two gene patterns were identified in this study. Both the high-expression modification pattern and the gene pattern exhibited worse OS. A prognostic RNAM-S model was constructed based on four featured genes (KIF20A, NR1I2, NR2F1 and PLOD2). Cellular experiments suggested significant dysregulation of the expression levels of these four genes. In addition, validation of the RNAM-S model using each data set showed good predictive performance of the model. The two groups of HCC patients (high and low RNAM-S groups) exhibited significant differences in immune status, CSC, mutation and drug sensitivity.

CONCLUSION

The findings of the study demonstrate the clinical value of RNA modifications, which provide new insights into the individualized treatment for patients with HCC.

TS-GCN: A novel tumor segmentation method integrating transformer and GCN

As one of the critical branches of medical image processing, the task of segmentation of breast cancer tumors is of great importance for planning surgical interventions, radiotherapy and chemotherapy. Breast cancer tumor segmentation faces several challenges, including the inherent complexity and heterogeneity of breast tissue, the presence of various imaging artifacts and noise in medical images, low contrast between the tumor region and healthy tissue, and inconsistent size of the tumor region. Furthermore, the existing segmentation methods may not fully capture the rich spatial and contextual information in small-sized regions in breast images, leading to suboptimal performance. In this paper, we propose a novel breast tumor segmentation method, called the transformer and graph convolutional neural (TS-GCN) network, for medical imaging analysis. Specifically, we designed a feature aggregation network to fuse the features extracted from the transformer, GCN and convolutional neural network (CNN) networks. The CNN extract network is designed for the image's local deep feature, and the transformer and GCN networks can better capture the spatial and context dependencies among pixels in images. By leveraging the strengths of three feature extraction networks, our method achieved superior segmentation performance on the BUSI dataset and dataset B. The TS-GCN showed the best performance on several indexes, with Acc of 0.9373, Dice of 0.9058, IoU of 0.7634, F1 score of 0.9338, and AUC of 0.9692, which outperforms other state-of-the-art methods. The research of this segmentation method provides a promising future for medical image analysis and diagnosis of other diseases.

Elevated ADAR expression is significantly linked to shorter overall survival and immune infiltration in patients with lung adenocarcinoma

To date, few studies have investigated whether the RNA-editing enzymes adenosine deaminases acting on RNA (ADARs) influence RNA functioning in lung adenocarcinoma (LUAD). To investigate the role of ADAR in lung cancer, we leveraged the advantages of The Cancer Genome Atlas (TCGA) database, from which we obtained transcriptome data and clinical information from 539 patients with LUAD. First, we compared ARAR expression levels in LUAD tissues with those in normal lung tissues using paired and unpaired analyses. Next, we evaluated the influence of ADARs on multiple prognostic indicators, including overall survival at 1, 3 and 5 years, as well as disease-specific survival and progression-free interval, in patients with LUAD. We also used Kaplan-Meier survival curves to estimate overall survival and Cox regression analysis to assess covariates associated with prognosis. A nomogram was constructed to validate the impact of the ADARs and clinicopathological factors on patient survival probabilities. The volcano plot and heat map revealed the differentially expressed genes associated with ADARs in LUAD. Finally, we examined ADAR expression versus immune cell infiltration in LUAD using Spearman's analysis. Using the Gene Expression Profiling Interactive Analysis (GEPIA2) database, we identified the top 100 genes most significantly correlated with ADAR expression, constructed a protein-protein interaction network and performed a Gene Ontology/Kyoto Encyclopedia of Genes and Genomes analysis on these genes. Our results demonstrate that ADARs are overexpressed in LUAD and correlated with poor patient prognosis. ADARs markedly increase the infiltration of T central memory, T helper 2 and T helper cells, while reducing the infiltration of immature dendritic, dendritic and mast cells. Most immune response markers, including T cells, tumor-associated macrophages, T cell exhaustion, mast cells, macrophages, monocytes and dendritic cells, are closely correlated with ADAR expression in LUAD.

Morphological Evaluation of Transscleral Laser Retinopexy in Rabbits: Comparison of Optical Coherence Tomography and Histologic Examinations

Transscleral retinopexy is a preventive technique used against retinal detachment. Fundus examination can allow the monitoring of morphological retinal changes in the progression of photocoagulation lesions, without offering details on the morphological changes by the retinal lesion. The aim of the study was to assess the progression of photocoagulation lesions induced by transscleral retinopexy (840 nm diode laser), by comparing the optical coherence tomography (OCT) and histological images over a period of six weeks on eight pigmented New Zealand healthy rabbits (four males and four females; n = 16 eyes). All rabbits underwent transscleral retinopexy on their left eye on day 0 (D0). Measurements of the photocoagulation lesions were obtained in vivo on D0, D7, D15, D21, and D42 by acquiring OCT images of both eyes from all rabbits. On D1, D7, D21, and D42, two rabbits were euthanized, and their eyes were enucleated. A significant effect by time on the decrease in the central retinal thickness of the photocoagulation lesion was observed from D1 to D7 (p = 0.001); however, no such effect was observed on the horizontal length ((HL) p = 0.584) of the lesion surface. The reliability between the OCT and histological measurements, which were evaluated using intraclass correlation coefficients, was excellent for measuring the retinal thickness at the center (ICC = 0.91, p < 0.001), moderate for the right side of the retinal lesions (ICC = 0.72, p = 0.006), and not significant for the left side and HL (p = 0.055 and 0.500, respectively). The morphological changes observed in the OCT and histopathological images of the photocoagulation lesions were qualitatively described over time. OCT is an effective tool for monitoring changes in photocoagulation lesions. Some measurements and qualitative changes showed an adequate correlation between the OCT and histological findings.

Multi-scale feature fusion for pavement crack detection based on Transformer

Automated pavement crack image segmentation presents a significant challenge due to the difficulty in detecting slender cracks on complex pavement backgrounds, as well as the significant impact of lighting conditions. In this paper, we propose a novel approach for automated pavement crack detection using a multi-scale feature fusion network based on the Transformer architecture, leveraging an encoding-decoding structure. In the encoding phase, the Transformer is leveraged as a substitute for the convolution operation, which utilizes global modeling to enhance feature extraction capabilities and address long-distance dependence. Then, dilated convolution is employed to increase the receptive field of the feature map while maintaining resolution, thereby further improving context information acquisition. In the decoding phase, the linear layer is employed to adjust the length of feature sequence output by different encoder block, and the multi-scale feature map is obtained after dimension conversion. Detailed information of cracks can be restored by fusing multi-scale features, thereby improving the accuracy of crack detection. Our proposed method achieves an F1 score of 70.84% on the Crack500 dataset and 84.50% on the DeepCrack dataset, which are improvements of 1.42% and 2.07% over the state-of-the-art method, respectively. The experimental results show that the proposed method has higher detection accuracy, better generalization and better crack detection results can be obtained under both high and low brightness conditions.

Abnormal static and dynamic amplitude of low-frequency fluctuations in multiple brain regions of methamphetamine abstainers

Methamphetamine (meth) addiction is a significant social and public health problem worldwide. The relapse rate of meth abstainers is significantly high, but the underlying physiological mechanisms are unclear. Therefore, in this study, we performed resting-state functional magnetic resonance imaging (rs-fMRI) analysis to detect differences in the spontaneous neural activity between the meth abstainers and the healthy controls, and identify the physiological mechanisms underlying the high relapse rate among the meth abstainers. The fluctuations and time variations in the blood oxygenation level-dependent (BOLD) signal of the local brain activity was analyzed from the pre-processed rs-fMRI data of 11 meth abstainers and 11 healthy controls and estimated the amplitude of low-frequency fluctuations (ALFF) and the dynamic ALFF (dALFF). In comparison with the healthy controls, meth abstainers showed higher ALFF in the anterior central gyrus, posterior central gyrus, trigonal-inferior frontal gyrus, middle temporal gyrus, dorsolateral superior frontal gyrus, and the insula, and reduced ALFF in the paracentral lobule and middle occipital gyrus. Furthermore, the meth abstainers showed significantly reduced dALFF in the supplementary motor area, orbital inferior frontal gyrus, middle frontal gyrus, medial superior frontal gyrus, middle occipital gyrus, insula, middle temporal gyrus, anterior central gyrus, and the cerebellum compared to the healthy controls ($ P < 0.05 $). These data showed abnormal spontaneous neural activity in several brain regions related to the cognitive, executive, and other social functions in the meth abstainers and potentially represent the underlying physiological mechanisms that are responsible for the high relapse rate. In conclusion, a combination of ALFF and dALFF analytical methods can be used to estimate abnormal spontaneous brain activity in the meth abstainers and make a more reasonable explanation for the high relapse rate of meth abstainers.

Fall detection based on dynamic key points incorporating preposed attention

Accidental falls pose a significant threat to the elderly population, and accurate fall detection from surveillance videos can significantly reduce the negative impact of falls. Although most fall detection algorithms based on video deep learning focus on training and detecting human posture or key points in pictures or videos, we have found that the human pose-based model and key points-based model can complement each other to improve fall detection accuracy. In this paper, we propose a preposed attention capture mechanism for images that will be fed into the training network, and a fall detection model based on this mechanism. We accomplish this by fusing the human dynamic key point information with the original human posture image. We first propose the concept of dynamic key points to account for incomplete pose key point information in the fall state. We then introduce an attention expectation that predicates the original attention mechanism of the depth model by automatically labeling dynamic key points. Finally, the depth model trained with human dynamic key points is used to correct the detection errors of the depth model with raw human pose images. Our experiments on the Fall Detection Dataset and the UP-Fall Detection Dataset demonstrate that our proposed fall detection algorithm can effectively improve the accuracy of fall detection and provide better support for elderly care.

Swept-Source OCT Mid-Peripheral Retinal Irregularity in Retinal Detachment and Posterior Vitreous Detachment Eyes

Irregularities in retinal shape have been shown to correlate with axial length, a major risk factor for retinal detachment. To further investigate this association, a comparison was performed of the swept-source optical coherence tomography (SS OCT) peripheral retinal shape of eyes that had either a posterior vitreous detachment (PVD) or vitrectomy for retinal detachment. The objective was to identify a biomarker that can be tested as a predictor for retinal detachment. Eyes with a PVD (N = 88), treated retinal detachment (N = 67), or retinal tear (N = 53) were recruited between July 2020 and January 2022 from hospital retinal clinics in South Australia. The mid-peripheral retina was imaged in four quadrants with SS OCT. The features explored were patient age, eye axial length, and retinal shape irregularity quantified in the frequency domain. A discriminant analysis classifier to identify retinal detachment eyes was trained with two-thirds and tested with one-third of the sample. Retinal detachment eyes had greater irregularity than PVD eyes. A classifier trained using shape features from the superior and temporal retina had a specificity of 84% and a sensitivity of 48%. Models incorporating axial length were less successful, suggesting peripheral retinal irregularity is a better biomarker for retinal detachment than axial length. Mid-peripheral retinal irregularity can identify eyes that have experienced a retinal detachment.

Retinal Alterations as Potential Biomarkers of Structural Brain Changes in Alzheimer’s Disease Spectrum Patients

Retinal imaging being a potential biomarker for Alzheimer’s disease is gradually attracting the attention of researchers. However, the association between retinal parameters and AD neuroimaging biomarkers, particularly structural changes, is still unclear. In this cross-sectional study, we recruited 25 cognitively impaired (CI) and 21 cognitively normal (CN) individuals. All subjects underwent retinal layer thickness and microvascular measurements with optical coherence tomography angiography (OCTA). Gray matter and white matter (WM) data such as T1-weighted magnetic resonance imaging and diffusion tensor imaging, respectively, were also collected. In addition, hippocampal subfield volumes and WM tract microstructural alterations were investigated as classical AD neuroimaging biomarkers. The microvascular and retinal features and their correlation with brain structural imaging markers were further analyzed. We observed a reduction in vessel density (VD) at the inferior outer (IO) sector (p = 0.049), atrophy in hippocampal subfield volumes, such as the subiculum (p = 0.012), presubiculum (p = 0.015), molecular_layer_HP (p = 0.033), GC-ML-DG (p = 0.043) and whole hippocampus (p = 0.033) in CI patients. Altered microstructural integrity of WM tracts in CI patients was also discovered in the cingulum hippocampal part (CgH). Importantly, we detected significant associations between retinal VD and gray matter volumes of the hippocampal subfield in CI patients. These findings suggested that the retinal microvascular measures acquired by OCTA may be markers for the early prediction of AD-related structural brain changes.

Facial expression recognition using lightweight deep learning modeling

Facial expression is a type of communication and is useful in many areas of computer vision, including intelligent visual surveillance, human-robot interaction and human behavior analysis. A deep learning approach is presented to classify happy, sad, angry, fearful, contemptuous, surprised and disgusted expressions. Accurate detection and classification of human facial expression is a critical task in image processing due to the inconsistencies amid the complexity, including change in illumination, occlusion, noise and the over-fitting problem. A stacked sparse auto-encoder for facial expression recognition (SSAE-FER) is used for unsupervised pre-training and supervised fine-tuning. SSAE-FER automatically extracts features from input images, and the softmax classifier is used to classify the expressions. Our method achieved an accuracy of 92.50% on the JAFFE dataset and 99.30% on the CK+ dataset. SSAE-FER performs well compared to the other comparative methods in the same domain.

Analysis of Retinal Microstructure in Eyes with Dissociated Optic Nerve Fiber Layer (DONFL) Appearance following Idiopathic Macular Hole Surgery: An Optical Coherence Tomography Study

(1) Purpose: This study aimed to evaluate morphological changes of the retina in eyes with dissociated optic nerve fiber layer (DONFL) appearance following internal limiting membrane (ILM) peeling for full-thickness idiopathic macular hole (IMH) on spectral-domain optical coherence tomography (SD-OCT). (2) Methods: We retrospectively analyzed 39 eyes of 39 patients with type 1 macular hole closure after a vitrectomy with ILM peeling procedure at a six-month minimum postoperative follow-up. The retinal thickness maps and cross-sectional OCT images were obtained from a clinical OCT device. The cross-sectional area of the retinal nerve fiber layer (RNFL) on cross-sectional OCT images was manually measured by ImageJ software. (3) Results: The inner retinal layers (IRLs) thickness thinned down much more in the temporal quadrant than in nasal quadrants at 2 and 6 months postoperatively (p < 0.001). However, the cross-sectional area of the RNFL did not change significantly at 2 and 6 months postoperatively (p > 0.05) when compared to preoperative data. In addition, the thinning of the IRL did not correlate with the best-corrected visual acuity (BCVA) at 6 months postoperatively. (4) Conclusions: The thickness of the IRL decreased in eyes with a DONFL appearance after ILM peeling for IMH. The thickness of the IRL decreased more in the temporal retina than in the nasal retina, but the change did not affect BCVA during the 6 months after surgery.

Different Morphology of Branching Neovascular Network in Polypoidal Choroidal Vasculopathy: A Swept-Source Optical Coherence Tomography Angiography Study

Purpose: To evaluate the classification system of branching neovascular network (BNN) morphology in polypoidal choroidal vasculopathy (PCV) patients based on swept-source optical coherence tomography (SS-OCT) and swept-source optical coherence tomography angiography (SS-OCTA), and analyze the morphological features in each group as potential prognostic features. Methods: A total of 32 PCV eyes were included in this retrospective study. SS-OCT and SS-OCTA images of 6 mm × 6 mm centered on the foveal of each eye were analyzed. PCV cases were classified into three types ("trunk", "glomeruli", and "stick" type) based on the morphological features of BNN. OCT and OCTA features were compared among the three groups. The correlation of OCT/OCTA features with visual acuity at 12 months after anti-VEGF treatment was also analyzed. Results: Type 1 group had the largest BNN area and the largest numbers of polypoidal lesions. Type 2 group has the largest pigment epithelial detachment (PED) area, PED volume, subretinal fluid (SRF) area, and SRF volume. Type 3 group had better baseline BCVA, the smallest BNN area, the smallest PED size, and the smallest SRF size. Type 1 was also featured by a clear break on Bruch's membrane which corresponded to the origin of neovascular tissue. BCVA at 12 months was not significantly different among groups. Baseline BCVA and baseline central macular thickness were correlated with the final BCVA. Conclusions: The current classification system based on BNN morphology on SS-OCTA was highly applicable and revealed distinct characteristics in each group. The BNN type was not correlated with BCVA at 12 months after treatment.

SAMS-Net: Fusion of attention mechanism and multi-scale features network for tumor infiltrating lymphocytes segmentation

Automatic segmentation of tumor-infiltrating lymphocytes (TILs) from pathological images is essential for the prognosis and treatment of cancer. Deep learning technology has achieved great success in the segmentation task. It is still a challenge to realize accurate segmentation of TILs due to the phenomenon of blurred edges and adhesion of cells. To alleviate these problems, a squeeze-and-attention and multi-scale feature fusion network (SAMS-Net) based on codec structure, namely SAMS-Net, is proposed for the segmentation of TILs. Specifically, SAMS-Net utilizes the squeeze-and-attention module with the residual structure to fuse local and global context features and boost the spatial relevance of TILs images. Besides, a multi-scale feature fusion module is designed to capture TILs with large size differences by combining context information. The residual structure module integrates feature maps from different resolutions to strengthen the spatial resolution and offset the loss of spatial details. SAMS-Net is evaluated on the public TILs dataset and achieved dice similarity coefficient (DSC) of 87.2% and Intersection of Union (IoU) of 77.5%, which improved by 2.5% and 3.8% compared with UNet. These results demonstrate the great potential of SAMS-Net in TILs analysis and can further provide important evidence for the prognosis and treatment of cancer.

Thermal Imaging to Assess the Health Status in Wildlife Animals under Human Care: Limitations and Perspectives

Promoting animal welfare in wildlife species under human care requires the implementation of techniques for continuously monitoring their health. Infrared thermography is a non-invasive tool that uses the radiation emitted from the skin of animals to assess their thermal state. However, there are no established thermal windows in wildlife species because factors such as the thickness or color of the skin, type/length of coat, or presence of fur can influence the readings taken to obtain objective, sensitive values. Therefore, this review aims to discuss the usefulness and application of the ocular, nasal, thoracic, abdominal, and podal anatomical regions as thermal windows for evaluating zoo animals' thermal response and health status. A literature search of the Web of Science, Science Direct, and PubMed databases was performed to identify relevant studies that used IRT with wild species as a complementary diagnostic tool. Implementing IRT in zoos or conservation centers could also serve as a method for determining and monitoring optimal habitat designs to meet the needs of specific animals. In addition, we analyze the limitations of using IRT with various wildlife species under human care to understand better the differences among animals and the factors that must be considered when using infrared thermography.