Mean 3D Research Articles

Predicting closed quarters battle capability – Examining the influence of personality, attentional ability, 2D:4D-ratio and mindfulness on tactical performance

ABSTRACT Close Quarters Battle (CQB) is an operational approach in confined spaces gaining increasing significance in urban combat missions. Due to its high psychophysiological demands, the CQB ability is an essential selection criterion for special forces. Until now, there has been no research on predictors of CQB capability. This study examined the influence of the Big Five personality traits, self-esteem, resilience, attentional ability, 2D:4D digit ratio, and mindfulness on the CQB performance. The German sample comprised a total of n = 45 individuals (n = 29police special forces; n = 16 unspecialized soldiers) who conducted psychometrics and a CQB test consisting of three scenarios. In these scenarios, two independent experts evaluated tactical behavior, weapon handling, gaze behavior, response time, and failures using a standardized behavioral observation instrument based on video recordings (external cameras and mobile eye-tracking). The results revealed that only extraversion predicted the CQB performance (β = -.40, p = .035). However, the mean 2D:4D ratio was strongly associated with gaze behavior (r = .45, p = .007), tactical behavior (r = .41, p = .019), and attentional ability (p = .57, p < .001). Surprisingly, the findings indicate that CQB, as a high-risk and analytical task, is better performed by introverted personnel.

Design, manufacturing, and multi-modal imaging of stereolithography 3D printed flexible intracranial aneurysm phantoms.

Physical vascular phantoms are instrumental in studying intracranial aneurysms and testing relevant imaging tools and training systems to provide improved clinical care. Current vascular phantom production methods have major limitations in capturing the biophysical and morphological characteristics of intracranial aneurysms with good fidelity and multi-modal imaging capacity. With stereolithography (SLA) 3D printing technology becoming more accessible, newer flexible and transparent printing materials with higher precision controls open the door for improving the efficiency and quality of producing anthropomorphic vascular phantoms but have rarely been explored for the application. This technical note intends to report the feasibility of using SLA 3D printing technology to manufacture flexible intracranial aneurysm phantoms with similar scales to the real anatomy, as well as their capacity for multi-modal flow imaging and analysis, including ultrasound flow imaging, high-speed filming, and particle image velocimetry analysis. We designed and 3D-printed two intracranial aneurysm phantoms with an SLA 3D printer using Formlabs Elastic 50A resin. By using a micropump to introduce cyclical flows in the phantoms, we first employed conventional Doppler and vector flow ultrasonography to observe and measure different fluidic properties. Then, a high-speed camera was used to record particles flowing within the phantom, and we further conducted a particle image velocimetry analysis, including the distribution of mean 2D velocity vectors, average velocity magnitudes, and the mean vorticity fields in the phantom for the high-speed imaging data. We successfully 3D-printed flexible intracranial aneurysm phantoms with similar dimensions to the real anatomy. Additionally, we validated the phantoms' ability to allow visualization, measurement, and analysis of flow dynamics based on both real-time ultrasound and optical imaging. Our proof-of-concept study illustrates that SLA 3D printing using commercial elastic resins can significantly contribute towards facilitating the fabrication of flexible intracranial aneurysms phantoms for training, research, and preoperative planning.

Analysis of Ratio of Length of 2nd Digit to 4th Digit (2D:4D) among Transgender Women (MtF) and Cisgender People in South Indian Population: A Cross-sectional Study

Introduction: Transgender refers to an individual whose gender identity differs from the sex that was assigned at birth. Transgender women (MtF) were assigned male at birth but later recognised themselves as female. The length of the index finger (2D) in males is shorter than the length of the ring finger (4D), resulting in a low finger ratio due to higher androgen levels, whereas in females, the ratio is high due to low androgen levels. The index to ring finger length ratio in males and females is influenced by testosterone hormone levels during early foetal life, exhibiting sexual dimorphism. The 2D:4D ratio serves as one of the determinants for future generations to identify the transgender women population. Aim: To determine the 2D:4D ratio among transgender women and the cisgender population. Materials and Methods: A cross-sectional study was conducted in the Department of Anatomy, Annapoorana Medical College and Hospitals, Vinayaka Mission’s Medical College Hospitals, Salem, Tamil Nadu, India, from September 2018 to October 2019. A total of 392 adult participants between 19 years and 60 years of age were selected. The samples of transwomen were collected with the help of the President of Salem Thirunangaigal Nala Sangam (STNS) and Koovagam Koothandavar Temple in Tamil Nadu. Cisgender samples were collected from volunteers in Salem, Tamil Nadu. The 2D and 4D digit lengths of both hands were measured using Digimizer software with the help of images obtained by a Canon 220 photo scanner. Another type of classification was done by naked eye analysis without any calculations, based on which the 2D:4D ratio among males, females and transgender women was categorised into types 1, 2 and 3. The paired t-test was used to compare the male, female and transwomen populations. Results: Out of 392 participants, 122 were cis females, 130 were cis males and 140 were transgender females. The p-value of the mean 2D:4D ratio was found to be significant in the right and left hands when comparing the 2D:4D ratio of males with females and transgender women with females. The p-value of the 2D:4D ratio for males with transgender women in the right and left hands was 0.451 and 0.943, respectively, which was statistically insignificant due to the high levels of androgen in these two groups. Conclusion: The present study helps scientists in the gender study field to advance their knowledge about transgender individuals, which can fill the gaps in available information and overcome existing lacunae in this field.

Coherent fading suppression of distributed acoustic sensing based on time–frequency domain linkage algorithm

Abstract Due to the inherent characteristic of coherent fading phenomenon, the performance of distributed acoustic sensing system will be severely degraded, producing anomalous information and positioning errors. In order to suppress the influence of this phenomenon, the data array of demodulated results is obtained after processing the collected distributed Rayleigh scattering signals along the sensing fiber, which can be utilized to construct a two-dimension (2D) image with the time–distance domain information. Thus, this allows us to remove the abnormal information that are introduced by fading noise. Based on this principle, a morphology compositional analysis (MCA) scheme, an effective time–frequency domain linkage algorithm, is applied to the constructed 2D images to enhance the sensing accuracy. Compared with traditional methods including the 2D mean value filter (MVF) and 2D wavelet transform (WT), the proposed method makes full use of the independence of image source property and abnormity feature so that the abnormal information caused by the coherent fading can be eliminated without deteriorating the source information along the sensing fiber. Experimental results show that the anomaly rate of the time–distance domain information along the sensing fiber for the proposed method, the MVF method and the WT method can be reduced by 92.43%, 65.05% and 70.18% respectively. In addition, the isolation degree of the MCA, MVF and WT methods are 41, 16.02 and 23.35 times higher than that treated by traditional method, respectively. Also, the stability of the time-domain vibration can be improved and the spatial resolution would not be influenced by the proposed method compared with the traditional method. This scheme will promote the development of distributed acoustic sensor system with high-accuracy and high-quality without any hardware modification.

The role of Cardiotrophin-1 and echocardiography in early detection of subclinical diabetic cardiomyopathy in children and adolescents with type 1 diabetes mellitus.

To assess the role of Cardiotrophin-1 (CT-1) and echocardiography in early detection of subclinical Diabetic Cardiomyopathy (DCM) in children with type 1 Diabetes Mellitus (T1D). This case-control study included two groups of children and adolescents aged between 7 and 18. Group (1) included forty patients with T1D (duration > 5 years) regularly followed at the children's hospital of Cairo University, and Group (2) included forty age and sex-matched healthy subjects as a control group. The serum level of CT-1 was measured, and conventional echocardiography, tissue Doppler imaging (TDI), and 2D speckle tracking echocardiography were performed. The level of CT-1 in the cases ranged from 11 to 1039.4 pg/ml with a median (IQR) of 19.4 (16.60-25.7) pg/ml, while its level in the control group ranged from 10.8 to 162.6 pg/ml with a median (IQR) of 20.2 (16.2-24.8) pg/ml. CT-1 levels showed no statistically significant difference between cases and controls. Patients had significantly higher mean left ventricle E/E' ratio (p<0.001), lower mean 2D global longitudinal strain (GLS) of the left ventricle (LV) (p<0.001), and lower mean GLS of the right ventricle (RV) (p<0.001) compared to controls. Ofpatients with diabetes, 75 % had LV diastolic dysfunction, 85 % had RV diastolic dysfunction, 97.5 % had LV systolic dysfunction, and 100 % had RV systolic dysfunction. Non-conventional echocardiography is important for early perception of subclinical DCM in patients with T1D. CT-1 was not specific for early detection of DCM.

DRPSO:A multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation

Colon adenocarcinoma (COAD) is a type of colon cancers with a high mortality rate. Its early symptoms are not obvious, and its late stage is accompanied by various complications that seriously endanger patients' lives. To assist in the early diagnosis of COAD and improve the detection efficiency of COAD, this paper proposes a multi-level threshold image segmentation (MIS) method based on an enhanced particle swarm algorithm for segmenting COAD images. Firstly, this paper proposes a multi-strategy fusion particle swarm optimization algorithm (DRPSO) with a replacement mechanism. The non-linear inertia weight and sine-cosine learning factors in DRPSO help balance the exploration and exploitation phases of the algorithm. The population reorganization strategy incorporating MGO enhances population diversity and effectively prevents the algorithm from stagnating prematurely. The mutation-based final replacement mechanism enhances the algorithm's ability to escape local optima and helps the algorithm to obtain highly accurate solutions. In addition, comparison experiments on the CEC2020 and CEC2022 test sets show that DRPSO outperforms other state-of-the-art algorithms in terms of convergence accuracy and speed. Secondly, by combining the non-local mean 2D histogram and 2D Renyi entropy, this paper proposes a DRPSO algorithm based MIS method, which is successfully applied to the segments the COAD pathology image problem. The results of segmentation experiments show that the above method obtains relatively higher quality segmented images with superior performance metrics: PSNR = 23.556, SSIM = 0.825, and FSIM = 0.922. In conclusion, the MIS method based on the DRPSO algorithm shows great potential in assisting COAD diagnosis and in pathology image segmentation.

Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells

Object-based radar rainfall nowcasting is a widely used technique for convective storm prediction. Currently, most existing object-based nowcasting methods primarily focus on predicting cell movements, neglecting the temporal evolution of cell properties such as size, shape, and intensity. Incorporating this evolution is critical for improving predictability in convective storms. While previous studies have used three-dimensional (3D) radar observations to capture vertical changes during convective cell formation, these efforts often analyse or reconstruct specific convective events. Integrating 3D radar information into operational object-based radar rainfall nowcasting remains an open challenge. This research addresses this challenge using deep learning (DL) techniques. More specifically, a DL-based prediction model is developed, which uses 2D and 3D cells' properties retrieved from 3D radar reflectivity data at the current time and across the past 15 min to predict the evolution of these properties over the next 15 min. This model could eventually be integrated into existing object-based nowcasting models. A total of 4708 cell lifecycles, extracted from high-resolution (5-min, 1-km, 24 levels at 0.5 km intervals) 3D radar data across the UK, are used to train the model, and a total of 1177 lifecycles are used for testing. The proposed model is shown to predict the evolution of single-core convective cells effectively, including changes in 2D projected geometry and mean 2D and 3D reflectivity. In particular, by incorporating information on the vertical evolution of convective cores, the prediction errors of mean reflectivity (in both 2D and 3D) can be reduced by approximately 50% at 15-min forecast lead time, as compared to a persistence forecast. Keywords: radar, tracking, convective cell, nowcasting, 3D, deep learning, lstm.

Morphological and dynamic analysis of the normal aortic valve with 4D computed tomography.

To evaluate the precise dimensions of the normal aortic root, especially the true aortic annulus, during the cardiac cycle using an innovative reconstruction method based on Multiphase Cardiac CT (MCCT) and to assess the feasibility and the reproducibility of this method for aortic root analysis. Between January 2019 and June 2021, 30 optimal consecutive ECG-gated MCCT of patients with normal tricuspid aortic valve were analyzed using an in-house software. Aortic annulus border was pinpointed on 9 reconstructed planes and the 3D coordinates of the 18 consecutive points were interpolated into a 3D curve using a cubic spline. Three additional planes were generated at the level of the left ventricular outflow tract, the level of the Valsalva sinus and the level of the sinotubular junction. This procedure was repeated for all the 10 temporal phases of the RR interval. The aortic annulus mean 3D and 2D areas were 7.67 ± 1.51 cm2 and 5.16 ± 1.40 cm2, respectively. Mean 2D diameter was 2.51 ± 0.23 cm. The mean global area expansion was 11.8 ± 3.5% and the mean perimeter expansion of 7.1 ± 2.6%. During the cardiac cycle, the left ventricle outflow tract expands, reaching its maximum surface at the end of diastole, followed by the aortic annulus, the valsalva sinuses and the sinotubular junction. The aorta changes from a clover shaped cone during diastole to more cylindrical shape during systole. Compared to the 3D measurements, the analysis of the virtual basal ring significantly underestimates the annulus area, perimeter, and mean diameter. 4D morphometric analysis enables to have a precise and reproducible evaluation of the aortic annulus. The aortic annulus and root are deformable structures that undergo a unique expansion sequence during the cardiac cycle which should be considered for procedural planning.

Validation of a deep learning model for automatic detection and quantification of five OCT critical retinal features associated with neovascular age-related macular degeneration

PurposeTo develop and validate a deep learning model for the segmentation of five retinal biomarkers associated with neovascular age-related macular degeneration (nAMD).Methods300 optical coherence tomography volumes from subject eyes with...

Association between the digit ratio (2D:4D), handgrip strength, wrist ratio, and body fat distribution in women with different carpal tunnel syndrome severity

BackgroundTo evaluate the relationship of the second to fourth digit ratio (2D:4D), a biomarker of prenatal sex hormone exposure, with wrist ratio (WR), wrist-palm ratio (WPR), body mass index (BMI), waist-hip ratio (WHR), and handgrip strength (HGS) in different carpal tunnel syndrome (CTS) severity. MethodThis study involved 90 female participants (aged 18 to 83) with CTS. According to CTS severity, the participants were divided into four groups: normal, mild, moderate, and severe. All parameters of hand anthropometry and body fat distribution were measured, and the mean 2D:4D, WR, WPR, HGS, BMI, and WHR values were compared based on CTS severity. Data were collected with a visual analog scale (VAS) for pain and a Likert (LS) scale for numbness severity. ResultsThe mean age, 2D:4D, WD, WW, WR, WPR, BMI, and HGS values showed a significant differences between CTS severity groups. We found that lower 2D:4D and higher WPR and BMI were associated with increased risk of CTS (AUC = 0.728) after removing the effect of age. Bilateral hands were affected in 38.9 % (70/180) of participants. Regression analysis showed that lower HGS can be used as independent variable for predicting the females having bilateral affected hands. The LS score was considerably higher in the severe and moderate groups. Also, the VAS score was significantly higher in the severe group. ConclusionThe findings of the study demonstrated an association between 2D:4D, WPR, and BMI among women, emphasizing the effect of intrauterine sex hormone exposure on late life CTS severity.

Multi-threshold Image Segmentation based on an improved Salp Swarm Algorithm: Case study of breast cancer pathology images

Breast cancer poses a significant risk to women’s health, and it is essential to provide proper diagnostic support. Medical image processing technology is a key component of all supporting diagnostic techniques, with Image Segmentation (IS) being one of its primary steps. Among various methods, Multilevel Image Segmentation (MIS) is considered one of the most effective and straightforward approaches. Many researchers have attempted to improve the quality of image segmentation by combining different metaheuristic algorithms with MIS. However, these methods often suffer from issues such as low convergence accuracy and a proclivity for converging towards Local Optima (LO). To overcome these challenges, this study introduces an integrated approach that combines the Salp Swarm Algorithm (SSA), Slime Mould Algorithm (SMA) and Differential Evolution (DE) algorithm. In this manuscript, we introduce an innovative hybrid MIS model termed SDSSA, which leverages elements from the SSA, SMA and DE algorithms. The SDSSA model fundamentally relies on non-local means 2D histogram and 2D Kapur’s entropy. To evaluate the proposed method effectively, we compare it initially with similar algorithms using the IEEE CEC2014 benchmark functions. The SDSSA showcases enhanced convergence velocity and precision relative to similar algorithms. Furthermore, this paper proposes an excellent MIS method. Subsequently, IS experiments were conducted separately at both low and high threshold levels. The test results demonstrate that the segmentation outcomes of MIS, at both low and high threshold levels, outperform other methods. This validates SDSSA as a superior segmentation technique that provides practical assistance for future research in breast cancer pathology image processing.

Face Image Segmentation Using Boosted Grey Wolf Optimizer.

Image segmentation methods have received widespread attention in face image recognition, which can divide each pixel in the image into different regions and effectively distinguish the face region from the background for further recognition. Threshold segmentation, a common image segmentation method, suffers from the problem that the computational complexity shows exponential growth with the increase in the segmentation threshold level. Therefore, in order to improve the segmentation quality and obtain the segmentation thresholds more efficiently, a multi-threshold image segmentation framework based on a meta-heuristic optimization technique combined with Kapur's entropy is proposed in this study. A meta-heuristic optimization method based on an improved grey wolf optimizer variant is proposed to optimize the 2D Kapur's entropy of the greyscale and nonlocal mean 2D histograms generated by image computation. In order to verify the advancement of the method, experiments compared with the state-of-the-art method on IEEE CEC2020 and face image segmentation public dataset were conducted in this paper. The proposed method has achieved better results than other methods in various tests at 18 thresholds with an average feature similarity of 0.8792, an average structural similarity of 0.8532, and an average peak signal-to-noise ratio of 24.9 dB. It can be used as an effective tool for face segmentation.

CLRM-09 MAPPING THE DISTRIBUTION AND CLINICAL ASSOCIATIONS OF BRAIN METASTASES IN RENAL CELL CARCINOMA

Abstract Brain metastases (BrM) commonly occur in renal cell carcinoma (RCC) and often present with hemorrhagic features. The distribution patterns of BrM in RCC and their associations with clinical outcomes remain unclear. We conducted a retrospective neuroanatomical distribution analysis combined with clinical review of our surgical RCC BrM cohort. Sixty-seven BrMs from 46patients (age 63±10years, 25males) were included. The majority of BrM (55%) were found in the left hemisphere, with frontal (45%), parietal (21%), and occipital (18%) lobes being the most commonly affected areas. Hemosiderin deposits, as detected by MRI, were present in 82% of BrM, while hemorrhage, as detected by CT, was present in 80% of BrM. Patients with solitary BrM had a higher KPS (p=0.006) and lower disease burden (p=0.004) compared to those with multiple BrM. Mean 1D and 2D(RANO) tumor dimensions were 2.0±1.1cm and 4.7±4.9cm2, respectively, and correlated with the neutrophil count at BrM diagnosis (r=0.3; p=0.01). Hemosiderin deposits were associated with larger tumors (2.6±1.0cm vs. 1.8±0.7cm, p=0.04), lower platelet counts (p=0.01), and higher hemoglobin levels (p=0.08). Shorter Brain Metastasis-Free Survival was associated with hemosiderin deposits on MRI (log-rank p=0.04) and renal vein thrombosis (RVT) in the primary specimen (log-rank p=0.002). RVT at primary diagnosis was associated with both hemosiderin deposits on MRI (p=0.05) and hemorrhage on CT (p=0.03), and CNS progression-free survival was shorter in patients with RVT (7.3±1.7 months vs. 21.0±4.7 months). When including only significant predictors, right hemispheric BrM lateralization (HR 2.6, p=0.06), hemosiderin deposits in BrM (HR 6.5, p=0.02), and IMDC risk groups (intermediate HR=2.9; poor-risk HR=25.6; p&amp;lt;0.04) were independent prognostic factors for shorter OS on multivariable analysis. Our results demonstrate that mapping the BrM distribution o in RCC can provide clinically relevant insights. Hemosiderin deposits in BrM may have prognostic implications. Further analyses are ongoing.

3D-validation of a simple tool to measure tibiofemoral axial rotation in tibial plateau fractures.

Rotated tibial plateau fractures (TPF) frequently involve multiple planes of movement, yet current presurgical assessment methods do not account for tibiofemoral axial rotation. This study introduces and validates a simple tool to measure rotation-the Gerdy-Tibial-Tuberosity-Surgical-Epicondylar-Axis (GTT-SEA) angle. Forty-seven preoperative 2D CT from a TPF database at a tertiary trauma center were retrieved, and 3D models reconstructed. Three observers made repeated 2D and 3D measurements of the GTT-SEA angle, spaced 4weeks apart, for 20 patients. Inter- and intra-observer agreement and 2D-3D correlation were calculated. A reference angle was defined from non-operated patients, to classify 28 patients with MRI into neutral, external rotation, and internal rotation groups. The classification agreement and soft tissue involvement between groups were analyzed. Mean 2D GTT-SEA angle was 17.65 ± 2.36° in non-operated patients, and 13.86 ± 3.90° in operated patients. 3D GTT-SEA angle was 18.92 ± 4.53° in non-operated patients, and 14.76 ± 6.03° in operated patients. 2D-3D correlation was moderate to good (ICC 0.64 ~ 0.83). Two-dimensional (ICC 0.70) and 3D (ICC 0.55) inter-observer agreements were moderate; 2D (ICC 0.82 ~ 0.88) and 3D (ICC 0.76 ~ 0.95) intra-observer agreements were good to excellent. Rotation classification agreement was slight (kappa 0.17) for 2D and good (kappa 0.76) for 3D. More popliteofibular ligament injury was detected in rotated knees (p = 0.016). The GTT-SEA angle offers simple, accessible, yet reliable measurement of tibiofemoral axial rotation. Though a true reference range remains to be determined, this tool adds valuable information to existing TPF classifications, potentially allowing assessment of soft tissue involvement in TPF. The GTT-SEA angle will benefit patients who sustain tibial plateau fractures, by allowing physicians to more accurately measure and plan for the injury in 3D, and raising suspicion for otherwise undetected soft tissue injuries, which can impact operative outcomes. • Traumatic fractures of the tibial plateau may contain rotation-induced soft tissue injuries. • A new tool to measure axial rotation between the femur and tibia was found to have moderate to excellent inter- and intra-rater reliability. • The tool may have potential in predicting soft tissue injury and assisting with the decision to receive MRI.

A solution to the stagnation of multi-verse optimization: An efficient method for breast cancer pathologic images segmentation

The multi-verse optimizer (MVO) algorithm has been applied to image segmentation, feature selection, engineering problems, and many other fields. MVO, like other metaheuristic algorithms, still has shortcomings, such as poor convergence speed and quickly falling into local optimum. To address these concerns, this paper proposes CBQMVO, extending the original MVO algorithm with three strategies: covariance matrix adaptation strategy (CMAES), biogeography-based learning strategy (BLS), quasi-reflected and quasi-opposition strategy (QROS). CMAES can make the algorithm approach quickly the current local optimal solution and accelerate the convergence. BLS can enrich the population’s diversity to discourage prematurely and assist the algorithm in jumping out of the local optimum. QROS can increase the probability of search particles falling near the optimal solution. A set of experiments were conducted to evaluate the performance of the CBQMVO. First, the original algorithm comparison experiment on IEEE CEC2014 includes strategy comparison, dimension comparison, exploration/exploitation balance, and population diversity experiments. Then, the advanced algorithm comparison experiment was carried out on IEEE CEC 2014. Furthermore, the champion algorithm comparison experiment was conducted on IEEE CEC2017 and IEEE CEC2020. A series of comparative experimental data demonstrate that CBQMVO has high performance, especially on some unimodal and complex competition functions. In addition, this paper also applied CBQMVO to implement Renyi’s entropy multilevel threshold image segmentation based on the non-local mean 2D histogram (RMIS-2D) on breast cancer pathologic images. Compared with other metaheuristic algorithms and Kapur’s entropy image segmentation, the proposed scheme in this paper has a better segmentation effect.

Flat does not mean 2D: Using X‐ray microtomography to study insect wings in 3D as a model for comparative studies

Abstract While not generally used for relatively flat structures, X‐ray microtomography can provide decisive insights for comparative anatomy and evolutionary studies of flat structures, thereby solving long‐standing ecological and evolutionary issues. This is particularly true for the study of insect wings, organs that have played a central role in the evolution and ecological success of insects, the first organisms to develop powered flight. We present the challenges inherent in studying insect wings by microtomography, as well as solutions to overcome them. We highlight the impact of insect preparation (i.e. dry or ethanol‐preserved specimens, complete specimens or only part of the wings, wings stained with iodine or with a 0.3% solution of phosphotungtic acid—PTA) and of the acquisition mode (phase contrast or not), on scan quality and ease of interpretation. We compare our results with those obtained by more ‘traditional’ techniques, based on examination of the wings with a binocular or SEM microscope. We show that X‐ray microtomography is effective in establishing vein homologies—a crucial step in many evolutionary studies in insects—by accessing the internal structures of the wing base itself, where veins originate. It can also be used for difficult tasks such as distinguishing between crossveins and main veins, establishing vein polarity (i.e. concave vs. convex veins) or vein fusions, situations that complicate the establishment of vein homology hypotheses and the completion of subsequent evolutionary studies. Unstained, ethanol‐preserved specimens give the least conclusive results, while dry specimens and ethanol‐preserved wings stained with PTA give the best results. We show that 3D tools provide crucial insights for the study of insect wings, illustrating how CT‐scan tools may contribute to a change in dimension, introducing quantification and functional approaches and hopefully stimulating new studies for other seemingly 2D organs in several other fields, including plant studies. We highlight, nonetheless, that this tool must be used in coordination with more traditional observational techniques, at least for insect wings, to provide a more complete picture of these fundamental organs.

Surface Smoothing by Gas Cluster Ion Beam Using Decreasing Three-Step Energy Treatment

A three-step treatment of Si wafers by gas cluster ion beam with decreasing energy was used to improve the performance of surface smoothing. First, a high energy treatment at 15 keV and an ion fluence of 2 × 1016 cm−2 was used to remove initial surface features (scratches). Next, treatments at 8 and 5 keV with the same fluences reduced the roughness that arose due to the formation of morphological features induced by the surface sputtering at the first high energy step. The surface morphology was characterized by the atomic force microscopy. The root mean square roughness Rq and 2D isotropic power spectral density functions were analyzed. For comparison, the smoothing performances of single-step treatments at 15, 8, and 5 keV were also studied. The lowest roughness values achieved for the single and three-step treatments were 1.06 and 0.65 nm, respectively.

An Efficient Approach to Mapping the Distribution of Renal Cell Carcinoma (RCC) Brain Metastases (BrM) (P10-13.003)

<h3>Objective:</h3> To assess the distribution of RCC BrM in clinical context. <h3>Background:</h3> RCC frequently presents with hemorrhagic BrMs. BrM distribution patterns and their associations with lab/clinical parameters are poorly understood. <h3>Design/Methods:</h3> We performed a retrospective analysis including RCC patients who underwent BrM resection at our institution. We reviewed gadolinium-enhanced MRI brain and CT head at the time of BrM diagnosis and assessed the presence of hemosiderin (MRI) or hemorrhage (CT) in the BrM. BrMs were marked in their corresponding anatomic location in a brain template. Public brain atlases (Faria and Liu 10.25790/bml0cm.109; Vachet, SCR_002606) were used for distribution analysis. Serum markers were correlated with imaging. <h3>Results:</h3> A total of 67 BM were analyzed from 46 patients (age 63±10years/35M), 23 synchronous BM, 9 patients had two, 6 had 3 concurrent BrMs. 97% of the lesions were spheric in shape. Mean 1D and 2D (RANO) tumor dimensions were 2.0±1.1cm and 4.7±4.9cm², respectively and correlated with neutrophil count at BrM diagnosis (r=0.3, p=0.01). Multilobar edema was seen in 36% of BrM. 82% of BrMs contained hemosiderin and 80% were hemorrhagic. Most BrMs were in the frontal (45%), parietal (21%) and occipital (18%) lobes. Posterior cerebral artery distribution contained 34% of BrM despite it supplying only 16% of brain volume. Higher platelet counts were associated with BrMs in the posterior circulation territories (p=0.05). BrM hemosiderin was associated with larger tumors (p=0.04), lower platelet counts (p=0.01) and higher hemoglobin (p=0.08). Renal vein thrombosis (RVT) at primary diagnosis was associated both BrM hemosiderin (p=0.05) and hemorrhage (p=0.03). CNS progression-free survival was shorter in those who had RVT (p=0.04), or had BrM hemosiderin (p=0.04). <h3>Conclusions:</h3> The presented mapping method offers an objective and efficient way to assess lesion distribution that reveals clinically relevant patterns. BrM hemosiderin deposits are linked to lower platelet counts and may provide prognostic information. Further analyses are underway. <b>Disclosure:</b> The institution of Dr. Kamson has received research support from Maryland Cigarette Restitution Fund. Mr. Feinaj has nothing to disclose. Dr. Elias has nothing to disclose. Mr. Yerrapragada has nothing to disclose. Dr. Bettegowda has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Bionaut Labs. Dr. Bettegowda has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Rodey, Dickason, Sloan, Akin &amp; Robb, P.A.. Dr. Bettegowda has stock in OrisDx. The institution of Dr. Bettegowda has received research support from NIH. Dr. Bettegowda has received intellectual property interests from a discovery or technology relating to health care. Mr. Singla has nothing to disclose. Yasser Ged has nothing to disclose.

Patient-specific uncertainty and bias quantification of non-transparent convolutional neural network model through knowledge distillation and Bayesian deep learning.

Assessing the reliability of convolutional neural network (CNN)-based CT imaging techniques is critical for reliable deployment in practice. Some evaluation methods exist but require full access to target CNN architecture and training data, something not available for proprietary or commercial algorithms. Moreover, there is a lack of systematic evaluation methods. To address these issues, we propose a patient-specific uncertainty and bias quantification (UNIQ) method that integrates knowledge distillation and Bayesian deep learning. Knowledge distillation creates a transparent CNN ("Student CNN") to approximate the target non-transparent CNN ("Teacher CNN"). Student CNN is built as a Bayesian-deep-learning-based probabilistic CNN that, for each input, always generates statistical distribution of the corresponding outputs, and characterizes predictive mean and two major uncertainties - data and model uncertainty. UNIQ was evaluated using a low-dose CT denoising task. Patient and phantom scans with routine-dose and synthetic quarter-dose were used to create training, validation, and testing sets. To demonstrate, Unet and Resnet were used as backbones of Teacher CNN and Student CNN respectively and were trained using independent training sets. Student Resnet was qualitatively and quantitatively evaluated. The pixel-wise predictive mean, data uncertainty, and model uncertainty from Student Resnet were very similar to the counterparts from Teacher Unet (mean-absolute-error: predictive mean 1.5HU, data uncertainty 1.8HU, model uncertainty 1.3HU; mean 2D correlation coefficient: total uncertainty 0.90, data uncertainty 0.86, model uncertainty 0.83). The proposed UNIQ can potentially systematically characterize the reliability of non-transparent CNN models used in CT.

Multi-threshold image segmentation based on an improved differential evolution: Case study of thyroid papillary carcinoma

The scholarly world has demonstrated an immense enthusiasm for medical image segmentation due to its intricate nature and critical role in medical diagnosis and treatment systems. Multi-threshold image segmentation (MTIS) is a popular technique for this purpose, due to its simplicity and straightforwardness. This paper presents an improved Differential Evolution (DE) algorithm called AGDE, which is based on MTIS and was used to evaluate its high capability at IEEE CEC 2017. Comparisons with classical and advanced algorithms were conducted as part of the experiments. An AGDE-based multi-threshold image segmentation method utilizing a non-local mean 2D histogram in combination with Rényi's entropy was applied to segment images from the Berkeley Segmentation Datasets 500 (BSDS500) and microscopic images of thyroid papillary carcinoma (TPC). The experimental results showed that the proposed image segmentation method outperformed its competitors, making it a promising approach for medical image segmentation.