Shape Prediction of Nasal Bones by Digital 2D-Photogrammetry of the Nose Based on Convolution and Back-Propagation Neural Network.

In rhinoplasty, it is necessary to consider the correlation between the anthropometric indicators of the nasal bone, so that it prevents surgical complications and enhances the patient's satisfaction. The penetrating form of high-energy electromagnetic radiation is highly impacted on human health, which has often raised concerns of alternative method for facial analysis. The critical stage to assess nasal morphology is the nasal analysis on its anthropology that is highly reliant on the understanding of the structural features of the nasal radix. For example, the shape and size of nasal bone features, skin thickness, and also body factors aggregated from different facial anthropology values. In medical diagnosis, however, the morphology of the nasal bone is determined manually and significantly relies on the clinician's expertise. Furthermore, the evaluation anthropological keypoint of the nasal bone is nonrepeatable and laborious, also finding widely differ and intralaboratory variability in the results because of facial soft tissue and equipment defects. In order to overcome these problems, we propose specialized convolutional neural network (CNN) architecture to accurately predict nasal measurement based on digital 2D photogrammetry. To boost performance and efficacy, it is deliberately constructed with many layers and different filter sizes, with less filters and optimizing parameters. Through its result, the back-propagation neural network (BPNN) indicated the correlation between differences in human body factors mentioned are height, weight known as body mass index (BMI), age, gender, and the nasal bone dimension of the participant. With full of parameters could the nasal morphology be diagnostic continuously. The model's performance is evaluated on various newest architecture models such as DenseNet, ConvNet, Inception, VGG, and MobileNet. Experiments were directly conducted on different facials. The results show the proposed architecture worked well in terms of nasal properties achieved which utilize four statistical criteria named mean average precision (mAP), mean absolute error (MAE), R-square (R 2), and T-test analyzed. Data has also shown that the nasal shape of Southeast Asians, especially Vietnamese, could be divided into different types in two perspective views. From cadavers for bony datasets, nasal bones can be classified into 2 morphological types in the lateral view which "V" shape was presented by 78.8% and the remains were "S" shape evaluated based on Lazovic (2015). With 2 angular dimension averages are 136.41 ± 7.99 and 104.25 ± 5.95 represented by the nasofrontal angle (g-n-prn) and the nasomental angle (n-prn-sn), respectively. For frontal view, classified by Hwang, Tae-Sun, et al. (2005), nasal morphology of Vietnamese participants could be divided into three types: type A was present in 57.6% and type B was present in 30.3% of the noses. In particular, types C, D, and E were not a common form of Vietnamese which includes the remaining number of participants. In conclusion, the proposed model performed the potential hybrid of CNN and BPNN with its application to give expected accuracy in terms of keypoint localization and nasal morphology regression. Nasal analysis can replace MRI imaging diagnostics that are reflected by the risk to human body.

Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer.

Tertiary lymphoid structures (TLS) are ectopic aggregates of lymphoid cells in inflamed, infected, or tumoral tissues that are easily recognized on an H&E histology slide as discrete entities, distinct from lymphocytes. TLS are associated with improved cancer prognosis but there is no standardised method available to quantify their presence. Previous studies have used immunohistochemistry to determine the presence of specific cells as a marker of the TLS. This has now been proven to be an underestimate of the true number of TLS. Thus, we propose a methodology for the automated identification and quantification of TLS, based on H&E slides. We subsequently determined the mathematical criteria defining a TLS. TLS regions were identified through a deep convolutional neural network and segmentation of lymphocytes was performed through an ellipsoidal model. This methodology had a 92.87% specificity at 95% sensitivity, 88.79% specificity at 98% sensitivity and 84.32% specificity at 99% sensitivity level based on 144 TLS annotated H&E slides implying that the automated approach was able to reproduce the histopathologists' assessment with great accuracy. We showed that the minimum number of lymphocytes within TLS is 45 and the minimum TLS area is 6,245µm2. Furthermore, we have shown that the density of the lymphocytes is more than 3 times those outside of the TLS. The mean density and standard deviation of lymphocytes within a TLS area are 0.0128/µm2 and 0.0026/µm2 respectively compared to 0.004/µm2 and 0.001/µm2 in non-TLS regions. The proposed methodology shows great potential for automated identification and quantification of the TLS density on digital H&E slides.

Computer-aided detection versus advanced imaging for detection of colorectal neoplasia: a systematic review and network meta-analysis.

BACKGROUND: Computer-aided detection (CADe) techniques based on artificial intelligence algorithms can assist endoscopists in detecting colorectal neoplasia. CADe has been associated with an increased adenoma detection rate, a key quality indicator, but the utility of CADe compared with existing advanced imaging techniques and distal attachment devices is unclear. METHODS: For this systematic review and network meta-analysis, we did a comprehensive search of PubMed/Medline, Embase, and Scopus databases from inception to Nov 30, 2020, for randomised controlled trials investigating the effectiveness of the following endoscopic techniques in detecting colorectal neoplasia: CADe, high definition (HD) white-light endoscopy, chromoendoscopy, or add-on devices (ie, systems that increase mucosal visualisation, such as full spectrum endoscopy [FUSE] or G-EYE balloon endoscopy). We collected data on adenoma detection rates, sessile serrated lesion detection rates, the proportion of large adenomas detected per colonoscopy, and withdrawal times. A frequentist framework, random-effects network meta-analysis was done to compare artificial intelligence with chromoendoscopy, increased mucosal visualisation systems, and HD white-light endoscopy (the control group). We estimated odds ratios (ORs) for the adenoma detection rate, sessile serrated lesion detection rate, and proportion of large adenomas detected per colonoscopy, and calculated mean differences for withdrawal time, with 95% CIs. Risk of bias and certainty of evidence were assessed with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. FINDINGS: 50 randomised controlled trials, comprising 34 445 participants, were included in our main analysis (six trials of CADe, 18 of chromoendoscopy, and 26 of increased mucosal visualisation systems). HD white-light endoscopy was the control technique in all 50 studies. Compared with the control technique, the adenoma detection rate was 7·4% higher with CADe (OR 1·78 [95% CI 1·44-2·18]), 4·4% higher with chromoendoscopy (1·22 [1·08-1·39]), and 4·1% higher with increased mucosal visualisation systems (1·16 [1·04-1·28]). CADe ranked as the superior technique for adenoma detection (with moderate confidence in hierarchical ranking); cross-comparisons of CADe with other imaging techniques showed a significant increase in the adenoma detection rate with CADe versus increased mucosal visualisation systems (OR 1·54 [95% CI 1·22-1·94]; low certainty of evidence) and with CADe versus chromoendoscopy (1·45 [1·14-1·85]; moderate certainty of evidence). When focusing on large adenomas (≥10 mm) there was a significant increase in the detection of large adenomas only with CADe (OR 1·69 [95% CI 1·10-2·60], moderate certainty of evidence) when compared to HD white-light endoscopy; CADe ranked as the superior strategy for detection of large adenomas. CADe also seemed to be the superior strategy for detection of sessile serrated lesions (with moderate confidence in hierarchical ranking), although no significant increase in the sessile serrated lesion detection rate was shown (OR 1·37 [95% CI 0·65-2·88]). No significant difference in withdrawal time was reported for CADe compared with the other techniques. INTERPRETATION: Based on the published literature, detection rates of colorectal neoplasia are higher with CADe than with other techniques such as chromoendoscopy or tools that increase mucosal visualisation, supporting wider incorporation of CADe strategies into community endoscopy services. FUNDING: None.

Turning Back the Clock: Artificial Intelligence Recognition of Age Reduction after Face-Lift Surgery Correlates with Patient Satisfaction.

BACKGROUND: Patients desire face-lifting procedures primarily to appear younger, more refreshed, and attractive. Because there are few objective studies assessing the success of face-lift surgery, the authors used artificial intelligence, in the form of convolutional neural network algorithms alongside FACE-Q patient-reported outcomes, to evaluate perceived age reduction and patient satisfaction following face-lift surgery. METHODS: Standardized preoperative and postoperative (1 year) images of 50 consecutive patients who underwent face-lift procedures (platysmaplasty, superficial musculoaponeurotic system-ectomy, cheek minimal access cranial suspension malar lift, or fat grafting) were used by four neural networks (trained to identify age based on facial features) to estimate age reduction after surgery. In addition, FACE-Q surveys were used to measure patient-reported facial aesthetic outcome. Patient satisfaction was compared to age reduction. RESULTS: The neural network preoperative age accuracy score demonstrated that all four neural networks were accurate in identifying ages (mean score, 100.8). Patient self-appraisal age reduction reported a greater age reduction than neural network age reduction after a face lift (-6.7 years versus -4.3 years). FACE-Q scores demonstrated a high level of patient satisfaction for facial appearance (75.1 ± 8.1), quality of life (82.4 ± 8.3), and satisfaction with outcome (79.0 ± 6.3). Finally, there was a positive correlation between neural network age reduction and patient satisfaction. CONCLUSION: Artificial intelligence algorithms can reliably estimate the reduction in apparent age after face-lift surgery; this estimated age reduction correlates with patient satisfaction. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning-A feasibility study.

Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics' average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors' regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation's efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).

Global evolution of research on pulmonary nodules: a bibliometric analysis.

Aim: To provide a historical and global picture of research concerning lung nodules, compare the contributions of major countries and explore research trends over the past 10 years. Methods: A bibliometric analysis of publications from Scopus (1970-2020) and Web of Science (2011-2020). Results: Publications about pulmonary nodules showed an enormous growth trend from 1970 to 2020. There is a high level of collaboration among the 20 most productive countries and regions, with the USA located at the center of the collaboration network. The keywords 'deep learning', 'artificial intelligence' and 'machine learning' are current hotspots. Conclusions: Abundant research has focused on pulmonary nodules. Deep learning is emerging as a promising tool for lung cancer diagnosis and management.

A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images.

The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework "MP-MitDet" for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier "MitosRes-CNN" to filter false mitoses. The performance of the proposed "MitosRes-CNN" is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.

A Systematic Approach of Data Collection and Analysis in Medical Imaging Research.

BACKGROUND: Obtaining the right image dataset for the medical image research systematically is a tedious task. Anatomy segmentation is the key step before extracting the radiomic features from these images. OBJECTIVE: The purpose of the study was to segment the 3D colon from CT images and to measure the smaller polyps using image processing techniques. This require huge number of samples for statistical analysis. Our objective was to systematically classify and arrange the dataset based on the parameters of interest so that the empirical testing becomes easier in medical image research. MATERIALS AND METHODS: This paper discusses a systematic approach of data collection and analysis before using it for empirical testing. In this research the image were considered from National Cancer Institute (NCI). TCIA from NCI has a vast collection of diagnostic quality images for the research community. These datasets were classified before empirical testing of the research objectives. The images in the TCIA collection were acquired as per the standard protocol defined by the American College of Radiology. Patients in the age group of 50-80 years were involved in various clinical trials (multicenter). The dataset collection has more than 10 billion of DICOM images of various anatomies. In this study, the number of samples considered for empirical testing was 300 (n) acquired from both supine and prone positions. The datasets were classified based on the parameters of interest. The classified dataset makes the dataset selection easier during empirical testing. The images were validated for the data completeness as per the DICOM standard of the 2020b version. A case study of CT Colonography dataset is discussed. CONCLUSION: With this systematic approach of data collection and classification, analysis will be become more easier during empirical testing.
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Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery.

Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensive imaging follow-up. Our objective in this study was to establish an algorithm by which to automate the volumetric measurement of vestibular schwannoma (VS) using a series of parametric MR images following radiosurgery. Based on a sample of 861 consecutive patients who underwent Gamma Knife radiosurgery (GKRS) between 1993 and 2008, the proposed end-to-end deep-learning scheme with automated pre-processing pipeline was applied to a series of 1290 MR examinations (T1W+C, and T2W parametric MR images). All of which were performed under consistent imaging acquisition protocols. The relative volume difference (RVD) between AI-based volumetric measurements and clinical measurements performed by expert radiologists were + 1.74%, - 0.31%, - 0.44%, - 0.19%, - 0.01%, and + 0.26% at each follow-up time point, regardless of the state of the tumor (progressed, pseudo-progressed, or regressed). This study outlines an approach to the evaluation of treatment responses via novel volumetric measurement algorithm, and can be used longitudinally following GKRS for VS. The proposed deep learning AI scheme is applicable to longitudinal follow-up assessments following a variety of therapeutic interventions.

The dermoscopic inverse approach significantly improves the accuracy of human readers for lentigo maligna diagnosis.

BACKGROUND: A recently introduced dermoscopic method for the diagnosis of early lentigo maligna (LM) is based on the absence of prevalent patterns of pigmented actinic keratosis and solar lentigo/flat seborrheic keratosis. We term this the inverse approach. OBJECTIVE: To determine whether training on the inverse approach increases the diagnostic accuracy of readers compared to classic pattern analysis. METHODS: We used clinical and dermoscopic images of histopathologically diagnosed LMs, pigmented actinic keratoses, and solar lentigo/flat seborrheic keratoses. Participants in a dermoscopy masterclass classified the lesions at baseline and after training on pattern analysis and the inverse approach. We compared their diagnostic performance among the 3 timepoints and to that of a trained convolutional neural network. RESULTS: The mean sensitivity for LM without training was 51.5%; after training on pattern analysis, it increased to 56.7%; and after learning the inverse approach, it increased to 83.6%. The mean proportions of correct answers at the 3 timepoints were 62.1%, 65.5, and 78.5%. The percentages of readers outperforming the convolutional neural network were 6.4%, 15.4%, and 53.9%, respectively. LIMITATIONS: The experimental setting and the inclusion of histopathologically diagnosed lesions only. CONCLUSIONS: The inverse approach, added to the classic pattern analysis, significantly improves the sensitivity of human readers for early LM diagnosis.

Magnetic resonance scoring system for assessment of adnexal masses: added value of diffusion-weighted imaging including apparent diffusion coefficient map.

OBJECTIVES: To validate prospectively the ADNEX magnetic resonance (MR) scoring system to assess adnexal masses and to evaluate a new, modified ADNEX MR scoring system that incorporates diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping. METHODS: Between January 2015 and September 2018, 323 consecutive women with adnexal masses diagnosed on transvaginal ultrasound (TVS) underwent standardized MR imaging (MRI) including diffusion and dynamic contrast-enhanced sequences. Of these, 131 underwent subsequent surgery. For interpretation of the MRI examinations, we applied the five-category ADNEX MR scoring system, along with a modified scoring system including DWI with ADC mapping. For both scoring systems, a score was given for all adnexal masses. Histological diagnosis was considered as the gold standard and lesions were classified as benign or malignant. The difference between the predictive values for diagnosing malignancy of the classical and modified scoring systems was assessed on the basis of the areas under the receiver-operating-characteristics (AUC) curves. The sensitivity and specificity for diagnosing malignancy of each score were also calculated. RESULTS: Among the 131 women with adnexal mass(es) diagnosed on TVS who underwent MRI and subsequent surgery, the surgery revealed 161 adnexal masses in 126 women; five women had no mass. Histological examination confirmed 161 adnexal masses, of which all had been detected on MRI: 32 malignant tumors, 15 borderline tumors, which were classified as part of the malignant group (n = 47), and 114 benign lesions. The AUC for prediction of a malignant lesion was 0.938 (95% CI, 0.902-0.975) using the classical ADNEX MR scoring system and 0.974 (95% CI, 0.953-0.996) using the modified scoring system. Pairwise comparison of these AUCs revealed a significant difference (P = 0.0032). The sensitivity and specificity for diagnosing malignancy with an ADNEX MR score of 4 or more were 95.5% and 86.6%, respectively, using the classic scoring system, and 95.7% and 93.3%, respectively, using the modified scoring system. CONCLUSION: DWI with ADC mapping could be integrated into the ADNEX MR scoring system to improve specificity, thereby potentially optimizing clinical management by avoiding unnecessary surgery. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.

Multiparametric Analysis of Focal Adhesions in Bidimensional Substrates.

Focal adhesions in planar substrates constitute an excellent cellular resource to evaluate different parameters related to cell morphology, cytoskeletal organization, and adhesive strength. However, their intrinsic heterogeneity in terms of size, molecular composition, orientation, and so on complicates their analysis. Here, we describe a simple and straightforward ImageJ/Fiji-based method to quantify several parameters that describe the morphology and relative composition of focal adhesions. This type of analysis can be implemented in various ways and become useful for drug and shRNA screenings.

Evidence for hypoxia increasing the tempo of evolution in glioblastoma.

BACKGROUND: Tumour hypoxia is associated with metastatic disease, and while there have been many mechanisms proposed for why tumour hypoxia is associated with metastatic disease, it remains unclear whether one precise mechanism is the key reason or several in concert. Somatic evolution drives cancer progression and treatment resistance, fuelled not only by genetic and epigenetic mutation but also by selection from interactions between tumour cells, normal cells and physical micro-environment. Ecological habitats influence evolutionary dynamics, but the impact on tempo of evolution is less clear. METHODS: We explored this complex dialogue with a combined clinical-theoretical approach by simulating a proliferative hierarchy under heterogeneous oxygen availability with an agent-based model. Predictions were compared against histology samples taken from glioblastoma patients, stained to elucidate areas of necrosis and TP53 expression heterogeneity. RESULTS: Results indicate that cell division in hypoxic environments is effectively upregulated, with low-oxygen niches providing avenues for tumour cells to spread. Analysis of human data indicates that cell division is not decreased under hypoxia, consistent with our results. CONCLUSIONS: Our results suggest that hypoxia could be a crucible that effectively warps evolutionary velocity, making key mutations more likely. Thus, key tumour ecological niches such as hypoxic regions may alter the evolutionary tempo, driving mutations fuelling tumour heterogeneity.

Mammographic density changes during neoadjuvant breast cancer treatment: NeoDense, a prospective study in Sweden.

OBJECTIVES: To assess if mammographic density (MD) changes during neoadjuvant breast cancer treatment and is predictive of a pathological complete response (pCR). METHODS: We prospectively included 200 breast cancer patients assigned to neoadjuvant chemotherapy (NACT) in the NeoDense study (2014-2019). Raw data mammograms were used to assess MD with a fully automated volumetric method and radiologists categorized MD using the Breast Imaging-Reporting and Data System (BI-RADS), 5th Edition. Logistic regression was used to calculate odds ratios (OR) for pCR comparing BI-RADS categories c vs. a, b, and d as well as with a 0.5% change in percent dense volume adjusting for baseline characteristics. RESULTS: The overall median age was 53.1 years, and 48% of study participants were premenopausal pre-NACT. A total of 23% (N = 45) of the patients accomplished pCR following NACT. Patients with very dense breasts (BI-RADS d) were more likely to have a positive axillary lymph node status at diagnosis: 89% of the patients with very dense breasts compared to 72% in the entire cohort. A total of 74% of patients decreased their absolute dense volume during NACT. The likelihood of accomplishing pCR following NACT was independent of volumetric MD at diagnosis and change in volumetric MD during treatment. No trend was observed between decreasing density according to BI-RADS and the likelihood of accomplishing pCR following NACT. CONCLUSIONS: The majority of patients decreased their MD during NACT. We found no evidence of MD as a predictive marker of pCR in the neoadjuvant setting.

Comprehensive Analysis of Radiomic Datasets by RadAR.

Quantitative analysis of biomedical images, referred to as radiomics, is emerging as a promising approach to facilitate clinical decisions and improve patient stratification. The typical radiomic workflow includes image acquisition, segmentation, feature extraction, and analysis of high-dimensional datasets. While procedures for primary radiomic analyses have been established in recent years, processing the resulting radiomic datasets remains a challenge due to the lack of specific tools for doing so. Here we present RadAR (Radiomics Analysis with R), a new software to perform comprehensive analysis of radiomic features. RadAR allows users to process radiomic datasets in their entirety, from data import to feature processing and visualization, and implements multiple statistical methods for analysis of these data. We used RadAR to analyze the radiomic profiles of more than 850 patients with cancer from publicly available datasets and showed that it was able to recapitulate expected results. These results demonstrate RadAR as a reliable and valuable tool for the radiomics community. SIGNIFICANCE: A new computational tool performs comprehensive analysis of high-dimensional radiomic datasets, recapitulating expected results in the analysis of radiomic profiles of >850 patients with cancer from independent datasets.

Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images.

Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. The proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.

Assessing Small Bowel Stricturing and Morphology in Crohn's Disease Using Semi-automated Image Analysis.

BACKGROUND: Evaluating structural damage using imaging is essential for the evaluation of small intestinal Crohn's disease (CD), but it is limited by potential interobserver variation. We compared the agreement of enterography-based bowel damage measurements collected by experienced radiologists and a semi-automated image analysis system. METHODS: Patients with small bowel CD undergoing a CT-enterography (CTE) between 2011 and 2017 in a tertiary care setting were retrospectively reviewed. CT-enterography studies were reviewed by 2 experienced radiologists and separately underwent automated computer image analysis using bowel measurement software. Measurements included maximum bowel wall thickness (BWT-max), maximum bowel dilation (DIL-max), minimum lumen diameter (LUM-min), and the presence of a stricture. Measurement correlation coefficients and paired t tests were used to compare individual operator measurements. Multivariate regression was used to model identification of strictures using semi-automated measures. RESULTS: In 138 studies, the correlation between radiologists and semi-automated measures were similar for BWT-max (r = 0.724, 0.702), DIL-max (r = 0.812, 0.748), and LUM-min (r = 0.428, 0.381), respectively. Mean absolute measurement difference between semi-automated and radiologist measures were no different from the mean difference between paired radiologists for BWT-max (1.26 mm vs 1.12 mm, P = 0.857), DIL-max (2.78 mm vs 2.67 mm, P = 0.557), and LUM-min (0.54 mm vs 0.41 mm, P = 0.596). Finally, models of radiologist-defined intestinal strictures using automatically acquired measurements had an accuracy of 87.6%. CONCLUSION: Structural bowel damage measurements collected by semi-automated approaches are comparable to those of experienced radiologists. Radiomic measures of CD will become an important new data source powering clinical decision-making, patient-phenotyping, and assisting radiologists in reporting objective measures of disease status.

Spatiotemporal Characterizations of Spontaneously Beating Cardiomyocytes with Adaptive Reference Digital Image Correlation.

We developed an Adaptive Reference-Digital Image Correlation (AR-DIC) method that enables unbiased and accurate mechanics measurements of moving biological tissue samples. We applied the AR-DIC analysis to a spontaneously beating cardiomyocyte (CM) tissue, and could provide correct quantifications of tissue displacement and strain for the beating CMs utilizing physiologically-relevant, sarcomere displacement length-based contraction criteria. The data were further synthesized into novel spatiotemporal parameters of CM contraction to account for the CM beating homogeneity, synchronicity, and propagation as holistic measures of functional myocardial tissue development. Our AR-DIC analyses may thus provide advanced non-invasive characterization tools for assessing the development of spontaneously contracting CMs, suggesting an applicability in myocardial regenerative medicine.