Three-Dimensional Vessel Segmentation in Whole-Tissue and Whole-Block Imaging Using a Deep Neural Network: Proof-of-Concept Study.

In the field of pathology, micro-computed tomography (micro-CT) has become an attractive imaging modality because it enables full analysis of the three-dimensional characteristics of a tissue sample or organ in a noninvasive manner. However, because of the complexity of the three-dimensional information, understanding would be improved by development of analytical methods and software such as those implemented for clinical CT. As the accurate identification of tissue components is critical for this purpose, we have developed a deep neural network (DNN) to analyze whole-tissue images (WTIs) and whole-block images (WBIs) of neoplastic cancer tissue using micro-CT. The aim of this study was to segment vessels from WTIs and WBIs in a volumetric segmentation method using DNN. To accelerate the segmentation process while retaining accuracy, a convolutional block in DNN was improved by introducing a residual inception block. Three colorectal tissue samples were collected and one WTI and 70 WBIs were acquired by a micro-CT scanner. The implemented segmentation method was then tested on the WTI and WBIs. As a proof-of-concept study, our method successfully segmented the vessels on all WTI and WBIs of the colorectal tissue sample. In addition, despite the large size of the images for analysis, all segmentation processes were completed in 10 minutes.

Acquisition and Analysis of Microcirculation Image in Septic Model Rats.

Background: Microcirculation is a vital sign that supplies oxygen and nutrients to maintain normal life activities. Sepsis typically influences the operation of microcirculation, which is recovered by the administration of medicine injection. Objective: Sepsis-induced variation and recovery of microcirculation are quantitatively detected using microcirculation images acquired by a non-contact imaging setup, which might assist the clinical diagnosis and therapy of sepsis. Methods: In this study, a non-contact imaging setup was first used to record images of microcirculation on the back of model rats. Specifically, the model rats were divided into three groups: (i) the sham group as a control group; (ii) the cecum ligation and puncture (CLP) group with sepsis; and (iii) the CLP+thrombomodulin (TM) group with sepsis and the application of TM alfa therapy. Furthermore, considering the sparsity of red blood cells (RBCs), the blood velocity is estimated by robust principal component analysis (RPCA) and U-net, and the blood vessel diameter is estimated by the contrast difference between the blood vessel and tissue. Results and Effectiveness: In the experiments, the continuous degradation of the estimated blood velocity and blood vessel diameter in the CLP group and the recovery after degradation of those in the CLP+TM group were quantitatively observed. The variation tendencies of the estimated blood velocity and blood vessel diameter in each group suggested the effects of sepsis and its corresponding therapy.

Connector sensors for permittivity-based thrombus monitoring in extracorporeal life support.

Extracorporeal circulation is vital in cardiovascular surgery, but thrombus formation at connector interface is a major threat. Optical coherence tomography (OCT) is presently used to monitor thrombogenesis at connectors, but it is expensive to install and complex to use. This study fabricated and evaluated a connector sensor for real-time permittivity-based thrombus monitoring at tube-connector interface. Computational simulations were initially done to pre-evaluate the applicability of connector sensor. The sensor was fabricated by incorporating two stainless steel electrodes on acrylic tube for measuring permittivity changes at the tube-connector interface. OCT images were also taken from the interface at intervals for comparisons. Results show that the sensor was able to detect thrombus formation at the interface in form of sudden rise in permittivity after time t = 9 min. The permittivity changes were confirmed by OCT images which showed thrombus formation after time t = 14 min implying that permittivity changes were due to regional aggregation of red blood cells. The connector sensor is therefore envisioned as an affordable alternative to OCT for real-time permittivity-based monitoring of thrombogenesis at tube-connector interface.

Stage-specific meniscal features predict progression of osteoarthritis of the knee: a retrospective cohort study using data from the osteoarthritis initiative.

BACKGROUND: In the progression of osteoarthritis (OA) of the knee, a correlation between meniscal posterior segment injuries and medial meniscal extrusion has been reported, but there have been few reports on the relationship with the meniscal shape. The purpose of this study was to clarify the features of the meniscal shape involved in the progression of knee OA. METHODS: Data were obtained from the Osteoarthritis Initiative (OAI) database. We defined two sets of subjects. One set included 455 knees of subjects whose OA grade on the Kellgren Lawrence (KL) scale progressed in 24 months from baseline and the other set consisted of 455 knees with no progression. The OA progressed subjects were divided to three groups: the "OA change group", KL0 and KL1 knees that progressed to KL2 and KL3; the "mild change group", KL2 knees that progressed to KL3; and the "severe change group", KL2 and KL3 knees that progressed to KL4. The no progression set was divided into three groups whose OA grade remained unchanged. We used magnetic resonance imaging data and manually measured seven items (longitudinal diameter [LD], anterior wedge thickness, anterior wedge width, posterior wedge width, posterior wedge thickness, anterior wedge angle, posterior wedge angle) from the sagittal slice and the extrusion from the coronal slice. These measurements were compared between knees with and without OA progression. RESULTS: In the "OA change group" and "mild change group", the anterior and posterior wedge widths and the extrusion were significantly larger, but the anterior and the posterior wedge angles were significantly smaller. In the "severe change group," the LD and the extrusion were significantly larger. In each group, there was no uniform tendency for the correlation coefficient of the parameters evaluated. CONCLUSIONS: Our findings suggested (1) a larger meniscal LD at the baseline predicted progression of knee OA after 24 months and (2) a larger meniscal width and smaller meniscal angle predicted progression of knee OA after 24 months.

Development of a 2D Image Reconstruction and Viewing System for Histological Images from Multiple Tissue Blocks: Towards High-Resolution Whole-Organ 3D Histological Images.

High-resolution 3D histology image reconstruction of the whole brain organ starts from reconstructing the high-resolution 2D histology images of a brain slice. In this paper, we introduced a method to automatically align the histology images of thin tissue sections cut from the multiple paraffin-embedded tissue blocks of a brain slice. For this method, we employed template matching and incorporated an optimization technique to further improve the accuracy of the 2D reconstructed image. In the template matching, we used the gross image of the brain slice as a reference to the reconstructed 2D histology image of the slice, while in the optimization procedure, we utilized the Jaccard index as the metric of the reconstruction accuracy. The results of our experiment on the initial 3 different whole-brain tissue slices showed that while the method works, it is also constrained by tissue deformations introduced during the tissue processing and slicing. The size of the reconstructed high-resolution 2D histology image of a brain slice is huge, and designing an image viewer that makes particularly efficient use of the computing power of a standard computer used in our laboratories is of interest. We also present the initial implementation of our 2D image viewer system in this paper.

Effects of arterial blood flow on walls of the abdominal aorta: distributions of wall shear stress and oscillatory shear index determined by phase-contrast magnetic resonance imaging.

Although abdominal aortic aneurysms (AAAs) occur mostly inferior to the renal artery, the mechanism of the development of AAA in relation to its specific location is not yet clearly understood. The objective of this study was to evaluate the hypothesis that even healthy volunteers may manifest specific flow characteristics of blood flow and alter wall shear or oscillatory shear stress in the areas where AAAs commonly develop. Eight healthy male volunteers were enrolled in this prospective study, aged from 24 to 27. Phase-contrast magnetic resonance imaging (MRI) was performed with electrocardiographic triggering. Flow-sensitive four-dimensional MR imaging of the abdominal aorta, with three-directional velocity encoding, including simple morphological image acquisition, was performed. Information on specific locations on the aortic wall was applied to the flow encodes to calculate wall shear stress (WSS) and oscillatory shear index (OSI). While time-framed WSS showed the highest peak of 1.14 ± 0.25 Pa in the juxtaposition of the renal artery, the WSS plateaued to 0.61 Pa at the anterior wall of the abdominal aorta. The OSI peaked distal to the renal arteries at the posterior wall of the abdominal aorta of 0.249 ± 0.148, and was constantly elevated in the whole abdominal aorta at more than 0.14. All subjects were found to have elevated OSI in regions where AAAs commonly occur. These findings indicate that areas of constant peaked oscillatory shear stress in the infra-renal aorta may be one of the factors that lead to morphological changes over time, even in healthy individuals.

Nonrigid registration method for longitudinal chest CT images in COVID-19.

Rationale and objectives: To analyze morphological changes in patients with COVID-19-associated pneumonia over time, a nonrigid registration technique is required that reduces differences in respiratory phase and imaging position and does not excessively deform the lesion region. A nonrigid registration method using deep learning was applied for lung field alignment, and its practicality was verified through quantitative evaluation, such as image similarity of whole lung region and image similarity of lesion region, as well as visual evaluation by a physician. Materials and methods: First, the lung field positions and sizes of the first and second CT images were roughly matched using a classical registration method based on iterative calculations as a preprocessing step. Then, voxel-by-voxel transformation was performed using VoxelMorph, a nonrigid deep learning registration method. As an objective evaluation, the similarity of the images was calculated. To evaluate the invariance of image features in the lesion site, primary statistics and 3D shape features were calculated and statistically analyzed. Furthermore, as a subjective evaluation, the similarity of images and whether nonrigid transformation caused unnatural changes in the shape and size of the lesion region were visually evaluated by a pulmonologist. Results: The proposed method was applied to 509 patient data points with high image similarity. The variances in histogram characteristics before and after image deformation were confirmed. Visual evaluation confirmed the agreement between the shape and internal structure of the lung field and the natural deformation of the lesion region. Conclusion: The developed nonrigid registration method was shown to be effective for quantitative time series analysis of the lungs.

Toward an application of automatic evaluation system for central facial palsy using two simple evaluation indices in emergency medicine.

A stroke is a medical emergency and thus requires immediate treatment. Paramedics should accurately assess suspected stroke patients and promptly transport them to a hospital with stroke care facilities; however, current assessment procedures rely on subjective visual assessment. We aim to develop an automatic evaluation system for central facial palsy (CFP) that uses RGB cameras installed in an ambulance. This paper presents two evaluation indices, namely the symmetry of mouth movement and the difference in mouth shape, respectively, extracted from video frames. These evaluation indices allow us to quantitatively evaluate the degree of facial palsy. A classification model based on these indices can discriminate patients with CFP. The results of experiments using our dataset show that the values of the two evaluation indices are significantly different between healthy subjects and CFP patients. Furthermore, our classification model achieved an area under the curve of 0.847. This study demonstrates that the proposed automatic evaluation system has great potential for quantitatively assessing CFP patients based on two evaluation indices.

Multi-modal evaluation of respiratory diaphragm motion in chronic obstructive pulmonary disease using MRI series and CT images.

PURPOSE: Chronic obstructive pulmonary disease (COPD), characterized by airflow limitation and breathing difficulty, is usually caused by prolonged inhalation of toxic substances or long-term smoking habits. Some abnormal features of COPD can be observed using medical imaging methods, such as magnetic resonance imaging (MRI) and computed tomography (CT). This study aimed to conduct a multi-modal analysis of COPD, focusing on assessing respiratory diaphragm motion using MRI series in conjunction with low attenuation volume (LAV) data derived from CT images. MATERIALS AND METHOD: This study utilized MRI series from 10 normal subjects and 24 COPD patients, along with thoracic CT images from the same patients. Diaphragm profiles in the sagittal thoracic MRI series were extracted using field segmentation, and diaphragm motion trajectories were generated from estimated diaphragm displacements via registration. Re-sliced sagittal CT images were used to calculate regional LAVs for four distinct lung regions. The similarities among diaphragm motion trajectories at various positions were assessed, and their correlations with regional LAVs were analyzed. RESULTS: Compared with the normal subjects, patients with COPD typically exhibited fewer similarities in diaphragm motion, as indicated by the mean normalized correlation coefficient of the vertical motion component (0.96 for normal subjects vs. 0.76 for severity COPD patients). This reduction was significantly correlated with the LAV% in the two lower lung regions with a regression coefficient of 0.81. CONCLUSION: Our proposed evaluation method may assist in the diagnosis and therapy planning for patients with COPD.

Observation and density estimation of a large number of skin capillaries using wide-field portable video capillaroscopy and semantic segmentation.

Significance: Skin capillaries are non-invasively observable; their structure and blood flow can reflect tissue and systemic conditions. Quantitative analysis of video-capillaroscopy images yields novel diagnostic methods. Because the capillary structure is heterogeneous, analyzing more capillaries can increase the evaluation reliability. Aim: We developed a system that can observe and quantify numerous capillaries and verified the performance on human skin. Approach: We developed a portable video-capillaroscope with a spatial resolution higher than 3.5 µm and a wide field of view (7.4 mm×5.5 mm) and a method to evaluate capillary numbers and areas using U-Net. The model was trained and tested with 22 and 11 cropped images (2.4 mm×1.9 mm) obtained from 11 participants, respectively. They were then applied to the 7.2 mm×5.3 mm images from four participants. Segmentation results were compared to ground-truth at the pixel level and capillary-region level. Results: Over 1000 capillaries were simultaneously observed using the proposed system. Although pixel-level segmentation performance was low [intersection over union (IoU) = 24.5%], the number and area could be estimated. These values differed among four participants and seven sites, and they changed after skin barrier destruction. Conclusions: The proposed system allows for observing and quantifying numerous skin capillaries simultaneously, suggesting its potential for evaluating tissue and systemic conditions.

Respiratory Diaphragm Motion-Based Asynchronization and Limitation Evaluation on Chronic Obstructive Pulmonary Disease.

Background: Chronic obstructive pulmonary disease (COPD) typically causes airflow blockage and breathing difficulties, which may result in the abnormal morphology and motion of the lungs or diaphragm. Purpose: This study aims to quantitatively evaluate respiratory diaphragm motion using a thoracic sagittal magnetic resonance imaging (MRI) series, including motion asynchronization and limitations. Method: First, the diaphragm profile is extracted using a deep-learning-based field segmentation approach. Next, by measuring the motion waveforms of each position in the extracted diaphragm profile, obvious differences in the independent respiration cycles, such as the period and peak amplitude, are verified. Finally, focusing on multiple breathing cycles, the similarity and amplitude of the motion waveforms are evaluated using the normalized correlation coefficient (NCC) and absolute amplitude. Results and Contributions: Compared with normal subjects, patients with severe COPD tend to have lower NCC and absolute amplitude values, suggesting motion asynchronization and limitation of their diaphragms. Our proposed diaphragmatic motion evaluation method may assist in the diagnosis and therapeutic planning of COPD.

Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19.

PURPOSE: As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatment and the appropriate allocation of limited healthcare resources remain unresolved issues. In this study we constructed a deep-learning (DL) model to predict the need for oxygen supplementation using clinical information and chest CT images of patients with COVID-19. MATERIALS AND METHODS: We retrospectively enrolled 738 patients with COVID-19 for whom clinical information (patient background, clinical symptoms, and blood test findings) was available and chest CT imaging was performed. The initial data set was divided into 591 training and 147 evaluation data. We developed a DL model that predicted oxygen supplementation by integrating clinical information and CT images. The model was validated at two other facilities (n = 191 and n = 230). In addition, the importance of clinical information for prediction was assessed. RESULTS: The proposed DL model showed an area under the curve (AUC) of 89.9% for predicting oxygen supplementation. Validation from the two other facilities showed an AUC > 80%. With respect to interpretation of the model, the contribution of dyspnea and the lactate dehydrogenase level was higher in the model. CONCLUSIONS: The DL model integrating clinical information and chest CT images had high predictive accuracy. DL-based prediction of disease severity might be helpful in the clinical management of patients with COVID-19.

Computer-aided diagnosis for screening of lower extremity lymphedema in pelvic computed tomography images using deep learning.

Lower extremity lymphedema (LEL) is a common complication after gynecological cancer treatment, which significantly reduces the quality of life. While early diagnosis and intervention can prevent severe complications, there is currently no consensus on the optimal screening strategy for postoperative LEL. In this study, we developed a computer-aided diagnosis (CAD) software for LEL screening in pelvic computed tomography (CT) images using deep learning. A total of 431 pelvic CT scans from 154 gynecological cancer patients were used for this study. We employed ResNet-18, ResNet-34, and ResNet-50 models as the convolutional neural network (CNN) architecture. The input image for the CNN model used a single CT image at the greater trochanter level. Fat-enhanced images were created and used as input to improve classification performance. Receiver operating characteristic analysis was used to evaluate our method. The ResNet-34 model with fat-enhanced images achieved the highest area under the curve of 0.967 and an accuracy of 92.9%. Our CAD software enables LEL diagnosis from a single CT image, demonstrating the feasibility of LEL screening only on CT images after gynecologic cancer treatment. To increase the usefulness of our CAD software, we plan to validate it using external datasets.

Measuring and imaging of transcutaneous bilirubin, hemoglobin, and melanin based on diffuse reflectance spectroscopy.

Significance: Evaluation of biological chromophore levels is useful for detection of various skin diseases, including cancer, monitoring of health status and tissue metabolism, and assessment of clinical and physiological vascular functions. Clinically, it is useful to assess multiple different chromophores in vivo with a single technique or instrument. Aim: To investigate the possibility of estimating the concentration of four chromophores, bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin from diffuse reflectance spectra in the visible region. Approach: A new diffuse reflectance spectroscopic method based on the multiple regression analysis aided by Monte Carlo simulations for light transport was developed to quantify bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin. Three different experimental animal models were used to induce hyperbilirubinemia, hypoxemia, and melanogenesis in rats. Results: The estimated bilirubin concentration increased after ligation of the bile duct and reached around 18 mg/dl at 50 h after the onset of ligation, which corresponds to the reference value of bilirubin measured by a commercially available transcutaneous bilirubin meter. The concentration of oxygenated hemoglobin and that of deoxygenated hemoglobin decreased and increased, respectively, as the fraction of inspired oxygen decreased. Consequently, the tissue oxygen saturation dramatically decreased. The time course of melanin concentration after depilation of skin on the back of rats was indicative of the supply of melanosomes produced by melanocytes of hair follicles to the growing hair shaft. Conclusions: The results of our study showed that the proposed method is capable of the in vivo evaluation of percutaneous bilirubin level, skin hemodynamics, and melanogenesis in rats, and that it has potential as a tool for the diagnosis and management of hyperbilirubinemia, hypoxemia, and pigmented skin lesions.

Development of a VR-based injection training system using a standardized patient.

We propose a VR based injection training system using Standardized Patient (SP) with an original haptic needle which can represent a haptic expression. SP is trained to realistically portray a real patient. In the proposed system, trainee can virtually puncture the SP using the haptic needle. In addition, the haptic needle can represent a haptic expression of needle insertion of the virtual anatomical model. By using the proposed system, trainee can feel virtual puncture as well as operating for real patient.

Vessel-based hybrid optic disk segmentation applied to mobile phone camera retinal images.

Precise detection of the optic disk (OD) is an important task in the diagnosis of diabetic retinopathy. To manage the massive diabetic population, there is a significant demand for efficient and remote retinal imaging techniques. In this regard, the use of handheld mobile cameras attached to a smartphone is a promising approach. However, smartphone retinal images are often of low quality, compared to those obtained on standard equipment. They also have a narrow field of view and an incomplete/unbalanced vessel structure. Hence, we propose a new, fully automatic hybrid method for OD localization (HLM). It is designed for and verified on mobile camera/smartphone retinal images. The HLM analyzes the vessel structure and finds the OD locations by using the exclusion method when an image has a complete vessel system, and a newly proposed line detection method, otherwise. For OD segmentation, an active contour model followed by the circle fitting approach is integrated into the HLM. The proposed method was tested on three mobile camera datasets and four datasets obtained by standard equipment. For mobile camera datasets, the HLM achieves an average accuracy of 98% for OD localization. The segmentation routine obtains an average precision of 92.64% and an average recall of 82.38%. Testing against the recent state-of-the-art methods on the standard datasets shows comparable performance. The proposed framework for OD localization and segmentation designed for and verified on mobile camera retinal datasets and standard datasets. (EM - "Exclusion Method", LDM - "Line Detection Method", OD - "Optic Disk" and PPV - "Positive Predictive Value").

Patch-based artifact reduction for three-dimensional volume projection data of sparse-view micro-computed tomography.

Micro-computed tomography (micro-CT) enables the non-destructive acquisition of three-dimensional (3D) morphological structures at the micrometer scale. Although it is expected to be used in pathology and histology to analyze the 3D microstructure of tissues, micro-CT imaging of tissue specimens requires a long scan time. A high-speed imaging method, sparse-view CT, can reduce the total scan time and radiation dose; however, it causes severe streak artifacts on tomographic images reconstructed with analytical algorithms due to insufficient sampling. In this paper, we propose an artifact reduction method for 3D volume projection data from sparse-view micro-CT. Specifically, we developed a patch-based lightweight fully convolutional network to estimate full-view 3D volume projection data from sparse-view 3D volume projection data. We evaluated the effectiveness of the proposed method using physically acquired datasets. The qualitative and quantitative results showed that the proposed method achieved high estimation accuracy and suppressed streak artifacts in the reconstructed images. In addition, we confirmed that the proposed method requires both short training and prediction times. Our study demonstrates that the proposed method has great potential for artifact reduction for 3D volume projection data under sparse-view conditions.

Semiautomatic assessment of respiratory dynamics using cine MRI in chronic obstructive pulmonary disease.

Purpose: The quantitative assessment of impaired lung motions and their association with the clinical characteristics of COPD patients is challenging. The aim of this study was to measure respiratory kinetics, including asynchronous movements, and to analyze the relationship between lung area and other clinical parameters. Materials and methods: This study enrolled 10 normal control participants and 21 COPD patients who underwent dynamic MRI and pulmonary function testing (PFT). The imaging program was implemented using MATLAB®. Each lung area was detected semi-automatically on a coronal image (imaging level at the aortic valve) from the inspiratory phase to the expiratory phase. The Dice index of the manual measurements was calculated, with the relationship between lung area ratio and other clinical parameters, including PFTs then evaluated. The asynchronous movements of the diaphragm were also evaluated using a sagittal image. Results: The Dice index for the lung region using the manual and semi-automatic extraction methods was high (Dice index = 0.97 ± 0.03). A significant correlation was observed between the time corrected lung area ratio and percentage of forced expiratory volume in 1 s (FEV1%pred) and residual volume percentage (RV%pred) (r = -0.54, p = 0.01, r = 0.50, p = 0.03, respectively). The correlation coefficient between each point of the diaphragm in the group with visible see-saw like movements was significantly lower than that in the group without see-saw like movements (value = -0.36 vs 0.95, p = 0.001). Conclusion: Semi-automated extraction of lung area from Cine MRI might be useful for detecting impaired respiratory kinetics in patients with COPD.