Enhancing Organizing Pneumonia Diagnosis: A Novel Super-token Transformer Approach for Masson Body Segmentation.

BACKGROUND/AIM: In this study, we introduce an innovative deep-learning model architecture aimed at enhancing the accuracy of detecting and classifying organizing pneumonia (OP), a condition characterized by the presence of Masson bodies within the alveolar spaces due to lung injury. The variable morphology of Masson bodies and their resemblance to adjacent pulmonary structures pose significant diagnostic challenges, necessitating a model capable of discerning subtle textural and structural differences. Our model incorporates a novel architecture that integrates advancements in three key areas: Semantic segmentation, texture analysis, and structural feature recognition. MATERIALS AND METHODS: We employed a dataset of whole slide imaging from 20 patients, totaling 100 slides of OP, segmented into training, validation, and testing sets to reflect real-world application scenarios. Our approach utilizes a modified multi-head self-attention mechanism combined with ResUNet for semantic segmentation, enhanced by superpixel concepts. This method facilitates the generation of representative token features through iterative super-token blocks, creating high-resolution token maps that leverage local and high-level feature information for improved accuracy. RESULTS: Benefiting from token features and distribution for enhanced texture alignment with fewer false-positives, the super-token transformer (STT) model achieved a mean intersection over union (mIOU) of 72.42%, with a sensitivity of 47.81%, specificity of 99.83%, positive predictive value of 64.03%, and negative predictive value of 99.94%, highlighting superior efficacy in Masson body segmentation in complex cross-tissue analyses. CONCLUSION: Our team developed an iterative learning model based on the STT approach, emphasizing token features of super token, including texture and distribution, that enable enhanced alignment with the unique textures of Masson bodies to improve sensitivity and mIOU, The development of this STT model presents a significant advancement in the field of medical image analysis for OP that offers a promising avenue for improving diagnostic precision and patient outcomes in pulmonary pathology.

Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features.

Goal: The early diagnosis and treatment of hepatitis is essential to reduce hepatitis-related liver function deterioration and mortality. One component of the widely-used Ishak grading system for the grading of periportal interface hepatitis is based on the percentage of portal borders infiltrated by lymphocytes. Thus, the accurate detection of lymphocyte-infiltrated periportal regions is critical in the diagnosis of hepatitis. However, the infiltrating lymphocytes usually result in the formation of ambiguous and highly-irregular portal boundaries, and thus identifying the infiltrated portal boundary regions precisely using automated methods is challenging. This study aims to develop a deep-learning-based automatic detection framework to assist diagnosis. Methods: The present study proposes a framework consisting of a Structurally-REfined Deep Portal Segmentation module and an Infiltrated Periportal Region Detection module based on heterogeneous infiltration features to accurately identify the infiltrated periportal regions in liver Whole Slide Images. Results: The proposed method achieves 0.725 in F1-score of lymphocyte-infiltrated periportal region detection. Moreover, the statistics of the ratio of the detected infiltrated portal boundary have high correlation to the Ishak grade (Spearman's correlations more than 0.87 with p-values less than 0.001) and medium correlation to the liver function index aspartate aminotransferase and alanine aminotransferase (Spearman's correlations more than 0.63 and 0.57 with p-values less than 0.001). Conclusions: The study shows the statistics of the ratio of infiltrated portal boundary have correlation to the Ishak grade and liver function index. The proposed framework provides pathologists with a useful and reliable tool for hepatitis diagnosis.

Design of HL7 FHIR Profiles for Pathology Reports Integrated with Pathology Images.

This paper describes the development of Health Level Seven Fast Healthcare Interoperability Resource (FHIR) profiles for pathology reports integrated with whole slide images and clinical data to create a pathology research database. A report template was designed to collect structured reports, enabling pathologists to select structured terms based on a checklist, allowing for the standardization of terms used to describe tumor features. We gathered and analyzed 190 non-small-cell lung cancer pathology reports in free text format, which were then structured by mapping the itemized vocabulary to FHIR observation resources, using international standard terminologies, such as the International Classification of Diseases, LOINC, and SNOMED CT. The resulting FHIR profiles were published as an implementation guide, which includes 25 profiles for essential data elements, value sets, and structured definitions for integrating clinical data and pathology images associated with the pathology report. These profiles enable the exchange of structured data between systems and facilitate the integration of pathology data into electronic health records, which can improve the quality of care for patients with cancer.

ADMM-SRNet: Alternating Direction Method of Multipliers Based Sparse Representation Network for One-Class Classification.

One-class classification aims to learn one-class models from only in-class training samples. Because of lacking out-of-class samples during training, most conventional deep learning based methods suffer from the feature collapse problem. In contrast, contrastive learning based methods can learn features from only in-class samples but are hard to be end-to-end trained with one-class models. To address the aforementioned problems, we propose alternating direction method of multipliers based sparse representation network (ADMM-SRNet). ADMM-SRNet contains the heterogeneous contrastive feature (HCF) network and the sparse dictionary (SD) network. The HCF network learns in-class heterogeneous contrastive features by using contrastive learning with heterogeneous augmentations. Then, the SD network models the distributions of the in-class training samples by using dictionaries computed based on ADMM. By coupling the HCF network, SD network and the proposed loss functions, our method can effectively learn discriminative features and one-class models of the in-class training samples in an end-to-end trainable manner. Experimental results show that the proposed method outperforms state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet-30 datasets under one-class classification settings. Code is available at https://github.com/nchucvml/ADMM-SRNet.

Multi-Scale Attention Convolutional Network for Masson Stained Bile Duct Segmentation from Liver Pathology Images.

In clinical practice, the Ishak Score system would be adopted to perform the evaluation of the grading and staging of hepatitis according to whether portal areas have fibrous expansion, bridging with other portal areas, or bridging with central veins. Based on these staging criteria, it is necessary to identify portal areas and central veins when performing the Ishak Score staging. The bile ducts have variant types and are very difficult to be detected under a single magnification, hence pathologists must observe bile ducts at different magnifications to obtain sufficient information. This pathologic examinations in routine clinical practice, however, would result in the labor intensive and expensive examination process. Therefore, the automatic quantitative analysis for pathologic examinations has had an increased demand and attracted significant attention recently. A multi-scale inputs of attention convolutional network is proposed in this study to simulate pathologists' examination procedure for observing bile ducts under different magnifications in liver biopsy. The proposed multi-scale attention network integrates cell-level information and adjacent structural feature information for bile duct segmentation. In addition, the attention mechanism of proposed model enables the network to focus the segmentation task on the input of high magnification, reducing the influence from low magnification input, but still helps to provide wider field of surrounding information. In comparison with existing models, including FCN, U-Net, SegNet, DeepLabv3 and DeepLabv3-plus, the experimental results demonstrated that the proposed model improved the segmentation performance on Masson bile duct segmentation task with 72.5% IOU and 84.1% F1-score.

Pathologic liver tumor detection using feature aligned multi-scale convolutional network.

The detection of the most common type of liver tumor, that is, hepatocellular carcinoma (HCC), is one essential step to liver pathology image analysis. In liver tissue, common cell change phenomena such as apoptosis, necrosis, and steatosis are similar in tumor and benign tissue. Hence, the detection of HCC may fail when the patches covered only limited tissue region without enough neighboring cell structure information. To address this problem, a Feature Aligned Multi-Scale Convolutional Network (FA-MSCN) architecture is proposed in this paper for automatic liver tumor detection based on whole slide images (WSI). The proposed network integrates the features obtained at different magnification levels to improve the detection performance by referencing more neighboring information. The FA-MSCN consists of two parallel convolutional networks in which one would extract high-resolution features and the other would extract low-resolution features by atrous convolution. The low-resolution features then go through central cropping, upsampling, and concatenation with high-resolution features for final classification. The experimental results demonstrated that Multi-Scale Convolutional Network (MSCN) improves the detection performance compared to Single-Scale Convolutional Network (SSCN), and that the FA-MSCN is superior to both SSCN and MSCN, demonstrating on HCC detection.

Texture-Map-Based Branch-Collaborative Network for Oral Cancer Detection.

The paper proposes an innovative deep convolutional neural network (DCNN) combined with texture map for detecting cancerous regions and marking the ROI in a single model automatically. The proposed DCNN model contains two collaborative branches, namely an upper branch to perform oral cancer detection, and a lower branch to perform semantic segmentation and ROI marking. With the upper branch the network model extracts the cancerous regions, and the lower branch makes the cancerous regions more precision. To make the features in the cancerous more regular, the network model extracts the texture images from the input image. A sliding window is then applied to compute the standard deviation values of the texture image. Finally, the standard deviation values are used to construct a texture map, which is partitioned into multiple patches and used as the input data to the deep convolutional network model. The method proposed by this paper is called texture-map-based branch-collaborative network. In the experimental result, the average sensitivity and specificity of detection are up to 0.9687 and 0.7129, respectively based on wavelet transform. And the average sensitivity and specificity of detection are up to 0.9314 and 0.9475, respectively based on Gabor filter.

Two-channel autofluorescence analysis for oral cancer.

We created a two-channel autofluorescence test to detect oral cancer. The wavelengths 375 and 460 nm, with filters of 479 and 525 nm, were designed to excite and detect reduced-form nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) autofluorescence. Patients with oral cancer or with precancerous lesions, and a control group with healthy oral mucosae, were enrolled. The lesion in the autofluorescent image was the region of interest. The average intensity and heterogeneity of the NADH and FAD were calculated. The redox ratio [(NADH)/(NADH + FAD)] was also computed. A quadratic discriminant analysis (QDA) was used to compute boundaries based on sensitivity and specificity. We analyzed 49 oral cancer lesions, 34 precancerous lesions, and 77 healthy oral mucosae. A boundary (sensitivity: 0.974 and specificity: 0.898) between the oral cancer lesions and healthy oral mucosae was validated. Oral cancer and precancerous lesions were also differentiated from healthy oral mucosae (sensitivity: 0.919 and specificity: 0.755). The two-channel autofluorescence detection device and analyses of the intensity and heterogeneity of NADH, and of FAD, and the redox ratio combined with a QDA classifier can differentiate oral cancer and precancerous lesions from healthy oral mucosae.

Novel quantitative analysis of autofluorescence images for oral cancer screening.

OBJECTIVES: VELscope® was developed to inspect oral mucosa autofluorescence. However, its accuracy is heavily dependent on the examining physician's experience. This study was aimed toward the development of a novel quantitative analysis of autofluorescence images for oral cancer screening. MATERIALS AND METHODS: Patients with either oral cancer or precancerous lesions and a control group with normal oral mucosa were enrolled in this study. White light images and VELscope® autofluorescence images of the lesions were taken with a digital camera. The lesion in the image was chosen as the region of interest (ROI). The average intensity and heterogeneity of the ROI were calculated. A quadratic discriminant analysis (QDA) was utilized to compute boundaries based on sensitivity and specificity. RESULTS: 47 oral cancer lesions, 54 precancerous lesions, and 39 normal oral mucosae controls were analyzed. A boundary of specificity of 0.923 and a sensitivity of 0.979 between the oral cancer lesions and normal oral mucosae were validated. The oral cancer and precancerous lesions could also be differentiated from normal oral mucosae with a specificity of 0.923 and a sensitivity of 0.970. CONCLUSION: The novel quantitative analysis of the intensity and heterogeneity of VELscope® autofluorescence images used in this study in combination with a QDA classifier can be used to differentiate oral cancer and precancerous lesions from normal oral mucosae.

Continuous Wave Spectroscopy with Diffusion Theory for Quantification of Optical Properties: Comparison Between Multi-distance and Multi-wavelength Data Fitting Methods.

Typically, continuous wave spectroscopy (CWS) can be used to accurately quantify biological tissue optical properties (µ a and µ s ') by employing the diffuse reflectance information acquired at multiple source-detector separations (multi-distance). On the other hand, sample optical properties can also be obtained by fitting multi-wavelength light reflectance acquired at a single source detector separation to the diffusion theory equation. To date, multi-wavelength and multi-distance methods have not yet been rigorously compared for their accuracy in quantification of the sample optical properties. In this investigation, we compared the accuracy of the two above-mentioned quantifying methods in the optical properties recovery. The liquid phantoms had µ a between 0.004 and 0.011 mm(-1) and µ s ' between 0.55 and 1.07 mm(-1) whose optical properties mimic the human breast. Multi-distance data and multi-wavelength data were fitted to the same diffusion equation for consistency. The difference between benchmark µ a and µ s ' and the fitted results, ΔError (ΔE) was used to evaluate the accuracy of the two methods. The results showed that either method yielded ΔE within 15-30 % when values were within certain limits to standard values applicable to µ s ' and µ a for human adipose tissue. Both methods showed no significant differences in ΔE values. Our results suggest that both multi-distance and multi-wavelength methods can yield similar reasonable optical properties in biological tissue with a proper calibration.

Gait and balance analysis for patients with Alzheimer's disease using an inertial-sensor-based wearable instrument.

Despite patients with Alzheimer's disease (AD) were reported of revealing gait disorders and balance problems, there is still lack of objective quantitative measurement of gait patterns and balance capability of AD patients. Based on an inertial-sensor-based wearable device, this paper develops gait and balance analyzing algorithms to obtain quantitative measurements and explores the essential indicators from the measurements for AD diagnosis. The gait analyzing algorithm is composed of stride detection followed by gait cycle decomposition so that gait parameters are developed from the decomposed gait details. On the other hand, the balance is measured by the sway speed in anterior-posterior (AP) and medial-lateral (ML) directions of the projection path of body's center of mass (COM). These devised gait and balance parameters were explored on twenty-one AD patients and fifty healthy controls (HCs). Special evaluation procedure including single-task and dual-task walking experiments for observing the cognitive function and attention is also devised for the comparison of AD and HC groups. Experimental results show that the wearable instrument with the designed gait and balance analyzing system is a promising tool for automatically analyzing gait information and balance ability, serving as assistant indicators for early diagnosis of AD.

Breast cancer detection of large size to DCIS by hypoxia and angiogenesis using NIRS.

This investigation aimed to test all tumor-bearing patients who undergo biopsy to see if angiogenesis and hypoxia can detect cancer. We used continuous-wave near-infrared spectroscopy (NIRS) to measure blood hemoglobin concentration to obtain blood volume or total hemoglobin [Hbtot] and oxygen saturation for the angiogenesis and hypoxic biomarkers. The contralateral breast was used as a reference to derive the difference from breast tumor as a difference in total hemoglobin Δ[HBtot] and a difference in deoxygenation Δ([Hb]-[HbO2]). A total of 91 invasive cancers, 26 DCIS, 45 fibroblastomas, 96 benign tumors excluding cysts, and 67 normal breasts were examined from four hospitals. In larger-size tumors, there is significantly higher deoxygenation in invasive and ductal carcinoma in situ (DCIS) than in that of benign tumors, but no significant difference was seen in smaller tumors of ≤ 1 cm. With the two parameters of high total hemoglobin and hypoxia score, the sensitivity and specificity of cancer detection were 60.3 % and 85.3 %, respectively. In summary, smaller-size tumors are difficult to detect with NIRS, whereas DCIS can be detected by the same total hemoglobin and hypoxic score in our study.

Novel recovery mechanism for the restoration of image contents in teleconsultation sessions.

In teleconsultation sessions, a critical dependency exists between the image contents and the type and sequential order of the image processing commands used by the various participants. Accordingly, for re-entrant/late users, a significant challenge exists in restoring the image contents of the teleconsultation session in such a way that all the participants maintain a consistent view of the medical images. In this paper, this problem is resolved using a novel recovery mechanism comprising two major components, namely an enhanced content-recording scheme designated as three-level indexing hierarchy (TIH) and a prioritized recovery policy. TIH maintains a record of all the commands which affect the appearance of each of medical images such that when a restoration process is required, these image-affect commands can be rapidly identified and transmitted to the user. As a result, a significant reduction can be gained in both the command identification/transmission time and the image restoration time compared to traditional recovery schemes, which restore the contents by re-executing all of the commands invoked during the course of the session. The prioritized recovery policy further reduces the time required for re-entrant/late users to catch up with the on-going session by utilizing the cross-linkage design within the TIH architecture to restore the foreground image (i.e. the image under current discussion) before the background images are restored (i.e. the remaining images in the session). To resolve the problem which arises when a background image is selected as the new foreground image before the restoration process is completed, the prioritized recovery policy maintains a set of resuming pointers for each re-entrant/late user to facilitate the process of suspending the current restoration process and switching to the restoration of the new foreground image. The evaluation results confirm that the TIH architecture and prioritized recovery policy yield a significant reduction in the recovery-latency delay compared to that required by traditional message-logging restoration systems.

An interaction-embedded HMM framework for human behavior understanding: with nursing environments as examples.

This paper presents an interaction-embedded hidden Markov model (IE-HMM) framework for automatically detecting and classifying individual human behaviors and group interactions. The proposed framework comprises a switch control (SC) module, an individual duration HMM (IDHMM) module, and an interaction-coupled duration HMM (ICDHMM) module. By analyzing the relative distances between the various participants in each scene, and monitoring the duration for which these distances are maintained, the SC module assigns each participant to an individual behavior unit (comprising a single participant) or an interaction behavior unit (comprising two or more participants). The individual behavior units are passed to the IDHMM module, which classifies the corresponding human behavior in accordance with the pose, motion, and duration information using duration HMM (DHMM). Similarly, the interaction behavior units are dispatched to the ICDHMM module, where the corresponding interaction mode is classified using an integrated scheme comprising multiple coupled-duration HMM (CDHMM), in which each state has an embedded coupled HMM (CHMM). The validity of the IE-HMM framework is confirmed by analyzing the human actions and interactions observed in a nursing home environment. The results confirm that the atomic behavior unit concept embedded in the SC module enables the IE-HMM framework to recognize multiple concurrent actions and interactions within a single scene. Overall, it is shown that the proposed framework has a recognition performance of 100% when applied to the analysis of individual human actions and 95% when applied to that of group interactions.

A novel indexing architecture for the provision of smart playback functions in collaborative telemedicine applications.

This paper presents a novel indexing architecture to support a range of smart playback functions in collaborative telemedicine systems. These functions include replaying the session from a specified point in time, replaying all the session segments controlled by a particular physician, replaying all the session segments relating to a specific medical image, and playing a montage of the entire session. Since the contents of telemedicine sessions vary over time depending on the particular commands invoked by the physician(s) during the session, when executing the smart playback functions, it is necessary to restore the target image contents to the appropriate condition before commencing the playback routine. In this study, this is achieved by using an indexing scheme designated as three-level indexing hierarchy (TIH) to search for the appropriate cut-in point in the session and to identify the commands which should be applied to restore the current image contents to their original condition at the cut-in point. In the proposed indexing scheme, the performance of the cut-in point determination process and the content restoration procedure is enhanced by maintaining a link between all the changes which take place in the image contents over the duration of the session and the commands which induce these changes. The evaluation results confirm that TIH outperforms existing scene-based retrieval systems in terms of both an improved computational efficiency and a lower storage requirement.

Wheelchair detection using cascaded decision tree.

One of the major goals of healthcare systems is to automatically monitor patients of special needs and alarm the caregivers for providing assistant. In this paper, an efficient single-camera multidirectional wheelchair detector based on a cascaded decision tree (CDT) is proposed to detect a wheelchair and its moving direction simultaneously from video frames for a healthcare system. Our approach combines a decision tree structure and boosted-cascade classifiers to construct a new CDT that can perform early confidence decisions in a hierarchical manner to rapidly reject nonwheelchairs and decide the moving directions. We also impose the tracking history to guide detection routes in the CDT to further reduce detection time and increase detection accuracy. The experiments show over 92% detection rate under cluttered scenes.

A visual context-awareness-based sleeping-respiration measurement system.

Due to the rapid growth of the elderly population, improving specific aspects of elderly healthcare has become more important. Sleeping care systems for the elderly are rare. In this paper, we propose a visual context-aware-based sleeping-respiration measurement system that measures the respiration information of elderly sleepers. Accurate respiration measurement requires considering all possible contexts for the sleeping person. The proposed system consists of a body-motion-context-detection subsystem, a respiration-context-detection subsystem, and a fast motion-vector-estimation-based respiration measurement subsystem. The system yielded accurate respiratory measurements for our study population.

Contrast enhancement and tissues classification of breast MRI using Kalman filter-based linear mixing method.

Much attention is currently focused on one of the newest breast examination techniques, breast MRI. Contrast-enhanced breast MRIs acquired by contrast injection have been shown to be very sensitive in the detection of breast cancer, but are also time-consuming and cause waste of medical resources. This paper therefore proposes the use of spectral signature detection technology, the Kalman filter-based linear mixing method (KFLM), which can successfully present the results as high-contrast images and classify breast MRIs into major tissues from four bands of breast MRIs. A series of experiments using phantom and real MRIs was conducted and the results compared with those of the commonly used c-means (CM) method and dynamic contrast-enhanced (DCE) breast MRIs for performance evaluation. After comparison with the CM algorithm and DCE breast MRIs, the experimental results showed that the high-contrast images generated by the spectral signature detection technology, the KFLM, were of superior quality.

Helicobacter pylori-related gastric histology classification using support-vector-machine-based feature selection.

This study presents a computer-aided diagnosis system using sequential forward floating selection (SFFS) with support vector machine (SVM) to diagnose gastric histology of Helicobacter pylori (H. pylori) from endoscopic images. To achieve this goal, candidate image features associated with clinical symptoms are extracted from endoscopic images. With these candidate features, the SFFS method is applied to select feature subsets, which perform the best classification results under SVM with respect to different histological features. By using the classifiers obtained from the feature subsets, a new diagnosis system is implemented to provide physicians with H. pylori -related histological results from endoscopic images.