Volumetric Scans Research Articles

Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network.

Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medical image segmentation methods have achieved state-of-the-art performance in various segmentation tasks. However, 3D medical images segmentation requires substantial global context and rich spatial semantic information, demanding much more GPU memory and computational resources. To address these challenges in 3D medical image segmentation, we propose a novel network- the MVEL-Net (Multi-view Ensemble Learning Network) for TMJ CBCT image segmentation. By resampling images along three dimensions, we generate multiple weak learners with different spatial semantic information. A subsequent strong learning network effectively integrates the outputs from these weak learners to achieve more accurate segmentation results. We evaluated our network model using a clinical dataset comprising 88 subjects with TMJ CBCT images. The average Dice similarity coefficient (DSC) was 0.9817 ± 0.0049, the average surface distance was 0.0540 ± 0.0179mm, and the 95% Hausdorff distance was 0.1743 ± 0.0550mm. Our proposed MVEL-Net demonstrates excellent segmentation performance on TMJ from CBCT images, while using fewer GPU memory resources compared to other 3D networks. The effectiveness of this method in capturing spatial context could be leveraged for tasks like organ segmentation from volumetric scans. This may facilitate wider adoption of AI-based solutions for automated analysis of 3D medical images.

Characterization and topographical analysis of choroidal caverns in young and healthy individuals.

To describe and clarify a possible pathogenetic origin for choroidal caverns in young healthy individuals through a topographical analysis using swept-source optical coherence tomography angiography (SS-OCTA). A cross-sectional evaluation of 44 healthy volunteers (44 eyes), aged 20-32 years with no systemic or ocular comorbidities. The topographical analysis of choroidal caverns was performed through a 15 × 15 mm volumetric scan cube using SS-OCTA (PLEX Elite 9000). The number, topographic localization within the perifovea and midperipheral retina, as well as the density of choroidal caverns in the area with the highest number, were analyzed. Factors influencing the presence of choroidal caverns were identified using a logistic regression model. The overall prevalence of choroidal caverns was 18.2% in our cohort. The subjects exhibiting choroidal caverns presented a greater AXL (+1.19 mm, 95%CI: 0.09, 2.29). Areas with the highest density of caverns presented an average of 1.98 ± 1.01 caverns/mm2. On a qualitative evaluation, the area with the highest density was along the inferior vascular arcade in 6/8 (75%) eyes. The main predictor of choroidal cavern development was represented by AXL (adjusted risk ratio of 2.13, P = 0.02). Choroidal caverns in young, healthy individuals may form due to mechanical stress in predisposed eyes, where an increased lateral and anteroposterior choroidal stretching due to globe elongation can result in the formation of these cavities. Future longitudinal studies are encouraged to understand their prognostic implications.

Deep Learning Methods for Detecting Chest X-Ray and CT Scan Abnormalities

Deep learning's quick development has created new opportunities to improve medical image analysis, especially in the identification of anomalies in chest CT and X-ray scans. This work investigates several deep learning techniques designed for this particular purpose to enhance the efficiency and accuracy of diagnosis in medical settings. We explore the use of 3D CNNs, transfer learning, and convolutional neural networks, or CNNs, for the analysis of volumetric CT scan information as well as 2D chest X-ray pictures. Comparative analyses show the benefits and drawbacks of various deep learning architectures for identifying a variety of anomalies, including tumors, tumors in the lungs, pneumonia, and other diseases. We also go over the significance of preprocessing methods, assessment metrics specifically designed for medical picture analysis, and dataset preparation. The results highlight how deep learning has the potential to revolutionize chest imaging diagnostics by facilitating the quicker and more accurate identification of anomalies, which will enhance patient outcomes and the effectiveness of healthcare delivery. To spur additional developments in the deep learning-powered analysis of medical images for chest problems, future research topics, and obstacles in this area are also covered.

Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans.

The application of machine learning to tasks involving volumetric biomedical imaging is constrained by the limited availability of annotated datasets of three-dimensional (3D) scans for model training. Here we report a deep-learning model pre-trained on 2D scans (for which annotated data are relatively abundant) that accurately predicts disease-risk factors from 3D medical-scan modalities. The model, which we named SLIViT (for 'slice integration by vision transformer'), preprocesses a given volumetric scan into 2D images, extracts their feature map and integrates it into a single prediction. We evaluated the model in eight different learning tasks, including classification and regression for six datasets involving four volumetric imaging modalities (computed tomography, magnetic resonance imaging, optical coherence tomography and ultrasound). SLIViT consistently outperformed domain-specific state-of-the-art models and was typically as accurate as clinical specialists who had spent considerable time manually annotating the analysed scans. Automating diagnosis tasks involving volumetric scans may save valuable clinician hours, reduce data acquisition costs and duration, and help expedite medical research and clinical applications.

Alzheimer's disease genetic risk and changes in brain atrophy and white matter hyperintensities in cognitively unimpaired adults.

Reduced brain volumes and more prominent white matter hyperintensities on MRI scans are commonly observed among older adults without cognitive impairment. However, it remains unclear whether rates of change in these measures among cognitively normal adults differ as a function of genetic risk for late-onset Alzheimer's disease, including APOE-ɛ4, APOE-ɛ2 and Alzheimer's disease polygenic risk scores (AD-PRS), and whether these relationships are influenced by other variables. This longitudinal study examined the trajectories of regional brain volumes and white matter hyperintensities in relationship to APOE genotypes (N = 1541) and AD-PRS (N = 1093) in a harmonized dataset of middle-aged and older individuals with normal cognition at baseline (mean baseline age = 66 years, SD = 9.6) and an average of 5.3 years of MRI follow-up (max = 24 years). Atrophy on volumetric MRI scans was quantified in three ways: (i) a composite score of regions vulnerable to Alzheimer's disease (SPARE-AD); (ii) hippocampal volume; and (iii) a composite score of regions indexing advanced non-Alzheimer's disease-related brain aging (SPARE-BA). Global white matter hyperintensity volumes were derived from fluid attenuated inversion recovery (FLAIR) MRI. Using linear mixed effects models, there was an APOE-ɛ4 gene-dose effect on atrophy in the SPARE-AD composite and hippocampus, with greatest atrophy among ɛ4/ɛ4 carriers, followed by ɛ4 heterozygouts, and lowest among ɛ3 homozygouts and ɛ2/ɛ2 and ɛ2/ɛ3 carriers, who did not differ from one another. The negative associations of APOE-ɛ4 with atrophy were reduced among those with higher education (P < 0.04) and younger baseline ages (P < 0.03). Higher AD-PRS were also associated with greater atrophy in SPARE-AD (P = 0.035) and the hippocampus (P = 0.014), independent of APOE-ɛ4 status. APOE-ɛ2 status (ɛ2/ɛ2 and ɛ2/ɛ3 combined) was not related to baseline levels or atrophy in SPARE-AD, SPARE-BA or the hippocampus, but was related to greater increases in white matter hyperintensities (P = 0.014). Additionally, there was an APOE-ɛ4 × AD-PRS interaction in relation to white matter hyperintensities (P = 0.038), with greater increases in white matter hyperintensities among APOE-ɛ4 carriers with higher AD-PRS. APOE and AD-PRS associations with MRI measures did not differ by sex. These results suggest that APOE-ɛ4 and AD-PRS independently and additively influence longitudinal declines in brain volumes sensitive to Alzheimer's disease and synergistically increase white matter hyperintensity accumulation among cognitively normal individuals. Conversely, APOE-ɛ2 primarily influences white matter hyperintensity accumulation, not brain atrophy. Results are consistent with the view that genetic factors for Alzheimer's disease influence atrophy in a regionally specific manner, likely reflecting preclinical neurodegeneration, and that Alzheimer's disease risk genes contribute to white matter hyperintensity formation.

A 3D hierarchical cross-modality interaction network using transformers and convolutions for brain glioma segmentation in MR images.

Precise glioma segmentation from multi-parametric magnetic resonance (MR) images is essential for brain glioma diagnosis. However, due to the indistinct boundaries between tumor sub-regions and the heterogeneous appearances of gliomas in volumetric MR scans, designing a reliable and automated glioma segmentation method is still challenging. Although existing 3D Transformer-based or convolution-based segmentation networks have obtained promising results via multi-modal feature fusion strategies or contextual learning methods, they widely lack the capability of hierarchical interactions between different modalities and cannot effectively learn comprehensive feature representations related to all glioma sub-regions. To overcome these problems, in this paper, we propose a 3D hierarchical cross-modality interaction network (HCMINet) using Transformers and convolutions for accurate multi-modal glioma segmentation, which leverages an effective hierarchical cross-modality interaction strategy to sufficiently learn modality-specific and modality-shared knowledge correlated to glioma sub-region segmentation from multi-parametric MR images. In the HCMINet, we first design a hierarchical cross-modality interaction Transformer (HCMITrans) encoder to hierarchically encode and fuse heterogeneous multi-modal features by Transformer-based intra-modal embeddings and inter-modal interactions in multiple encoding stages, which effectively captures complex cross-modality correlations while modeling global contexts. Then, we collaborate an HCMITrans encoder with a modality-shared convolutional encoder to construct the dual-encoder architecture in the encoding stage, which can learn the abundant contextual information from global and local perspectives. Finally, in the decoding stage, we present a progressive hybrid context fusion (PHCF) decoder to progressively fuse local and global features extracted by the dual-encoder architecture, which utilizes the local-global context fusion (LGCF) module to efficiently alleviate the contextual discrepancy among the decoding features. Extensive experiments are conducted on two public and competitive glioma benchmark datasets, including the BraTS2020 dataset with 494 patients and the BraTS2021 dataset with 1251 patients. Results show that our proposed method outperforms existing Transformer-based and CNN-based methods using other multi-modal fusion strategies in our experiments. Specifically, the proposed HCMINet achieves state-of-the-art mean DSC values of 85.33% and 91.09% on the BraTS2020 online validation dataset and the BraTS2021 local testing dataset, respectively. Our proposed method can accurately and automatically segment glioma regions from multi-parametric MR images, which is beneficial for the quantitative analysis of brain gliomas and helpful for reducing the annotation burden of neuroradiologists.

Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning

BackgroundDeep learning (DL) is promising to detect glaucoma. However, patients’ privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model...

Dynamic evaluation of corneal cross-linking and osmotic diffusion effects using optical coherence elastography

Dynamic deformation events induced by osmosis or photochemical stiffening substantially influence geometrical and mechanical assessments in post-mortem corneas, therefore need to be carefully monitored in experimental settings. In this study, we employed optical coherence elastography (OCE) to quantify dynamic deformation processes at high resolution in freshly enucleated porcine corneas. Osmotic effects were studied by immerging n = 9 eyes in preservation media of three different tonicities. Dynamic processes underlying corneal cross-linking (CXL) were studied by subjecting n = 6 eyes to standard Dresden treatment, while three control groups were used. The entire procedures were performed under an OCE setup during up to 80 min, acquiring a volumetric scan every 20 s. Changes in OCE-derived axial deformations were incrementally calculated between consecutive scans. Preservation conditions had a strong influence on the observed strain patterns, which were consistent with the tonicity of the medium (swelling in hypotonic, deswelling in hypertonic environment). In the CXL group, we observed deswelling of the anterior stroma 10 min after starting the UV irradiation, which was not observed in any control group (p = 0.007). The presented results proved OCE to be a valuable technique to quantify subtle dynamic biomechanical alterations in the cornea resulting from CXL and preservation solutions.

A 2.5D Self-Training Strategy for Carotid Artery Segmentation in T1-Weighted Brain Magnetic Resonance Images.

Precise annotations for large medical image datasets can be time-consuming. Additionally, when dealing with volumetric regions of interest, it is typical to apply segmentation techniques on 2D slices, compromising important information for accurately segmenting 3D structures. This study presents a deep learning pipeline that simultaneously tackles both challenges. Firstly, to streamline the annotation process, we employ a semi-automatic segmentation approach using bounding boxes as masks, which is less time-consuming than pixel-level delineation. Subsequently, recursive self-training is utilized to enhance annotation quality. Finally, a 2.5D segmentation technique is adopted, wherein a slice of a volumetric image is segmented using a pseudo-RGB image. The pipeline was applied to segment the carotid artery tree in T1-weighted brain magnetic resonance images. Utilizing 42 volumetric non-contrast T1-weighted brain scans from four datasets, we delineated bounding boxes around the carotid arteries in the axial slices. Pseudo-RGB images were generated from these slices, and recursive segmentation was conducted using a Res-Unet-based neural network architecture. The model's performance was tested on a separate dataset, with ground truth annotations provided by a radiologist. After recursive training, we achieved an Intersection over Union (IoU) score of (0.68 ± 0.08) on the unseen dataset, demonstrating commendable qualitative results.

Influence of hydrogen bonds on state diagrams of cryoprotectant solutions

BACKGROUND: Transformation of state diagrams of cryoprotectant solutions under the influence of weak intramolecular interactions was considered. MATERIALS AND METHODS: Phase states of aqueous glycerol and DMSO solutions within temperature range +25???−150 °?? were studied using method of volumetric scanning tensodilatometry. Temperatures below which hydrogen bonds significantly affect crystallization-melting kinetics of such solutions were determined. RESULTS: Principles for plotting of state diagram for binary solutions with weak intermolecular interaction of the components were set up. The study demonstrates that in such solutions formation of clusters based on ice microcrystals and cryoprotectant occurs. Based on the obtained results, state diagrams for glycerol and DMSO aqueous solutions were plotted. These diagrams include area of cluster phase existence and differ fundamentally from those describing eutectic crystallization. CONCLUSION: Nanostructures occurring in cryoprotectant solutions during their cooling were analyzed. Difference between these structures and classical solid phase eutectics were demonstrated.

Three-Dimensional X-Ray Computed Tomography Image Segmentation and Point Cloud Reconstruction for Internal Defect Identification in Laser Powder Bed Fused Parts

Abstract Defects shape, volume, and orientation all have a direct impact on the mechanical properties of Laser Powder Bed Fused (L-PBF-ed) parts. Therefore, it is necessary to evaluate and analyze the three-dimensional (3D) geometrical characteristics of these defects. X-ray Computed Tomography (XCT) can reveal an object's internal structure by volumetric scanning through its building direction. Point clouds are 3D data that can be extracted from the stack of XCT images taken from a part to perform further analysis. This study presents a novel approach for 3D segmentation and geometrical analysis of L-PBF defect structures from XCT images. The proposed method integrates Voronoi labeling and 3D point cloud reconstruction to reveal individual defect characteristics from the XCT image stack of a part. A case study showed the proposed methodology's effectiveness in identifying and characterizing defect regions in L-PBF-ed Cobalt-Chrome (CoCr) parts.

Comparison of line-of-sight wind speed measurements from an X-band radar and a long-range scanning lidar

As a still novel wind measurement technology, a dual-Doppler X-band wind radar system has been a substantial element of the full-scale onshore campaign AWAKEN (The American WAKE experimeNt). In order to select suitable further applications in the future and, particularly, the most optimal use cases in the wind industry for this technology, a line-of-sight wind speed verification campaign using a co-located scanning lidar as reference was set up as part of the AWAKEN campaign. The wind radar scanned in azimuth sector or plan position indicator (PPI) mode, with multiple elevations (volumetric PPI scan), while the scanning lidar remained fixed in a specific position during the verification campaign. Considering the individual spatial and temporal resolutions of the two systems, the closest points from the radar scanned volumes were compared with measurements from the scanning lidar after threshold-based and statistical data quality control. For a linear regression with 30-minute resolution data collected at 2 km range, a coefficient of determination of R 2 = 0.99 was found. Radar mean values, binned according to reference wind speed, do lie in part within the reference uncertainty but not consistently for the investigated range of line-of-sight wind speeds. Part of the reference uncertainty is hereby also associated with the procedure of comparison but kept as low as possible by optimizing the verification setup and procedure.

The sex of organ geometry

Organs have a distinctive yet often overlooked spatial arrangement in the body1–5. We propose that there is a logic to the shape of an organ and its proximity to its neighbours. Here, by using volumetric scans of many Drosophila melanogaster flies, we develop methods to quantify three-dimensional features of organ shape, position and interindividual variability. We find that both the shapes of organs and their relative arrangement are consistent yet differ between the sexes, and identify unexpected interorgan adjacencies and left–right organ asymmetries. Focusing on the intestine, which traverses the entire body, we investigate how sex differences in three-dimensional organ geometry arise. The configuration of the adult intestine is only partially determined by physical constraints imposed by adjacent organs; its sex-specific shape is actively maintained by mechanochemical crosstalk between gut muscles and vascular-like trachea. Indeed, sex-biased expression of a muscle-derived fibroblast growth factor-like ligand renders trachea sexually dimorphic. In turn, tracheal branches hold gut loops together into a male or female shape, with physiological consequences. Interorgan geometry represents a previously unrecognized level of biological complexity which might enable or confine communication across organs and could help explain sex or species differences in organ function.

Three scans are better than two for follow-up: An automatic method for finding missed and misidentified lesions in cross-sectional follow-up of oncology patients

PurposeMissed and misidentified neoplastic lesions in longitudinal studies of oncology patients are pervasive and may affect the evaluation of the disease status. Two newly identified patterns of lesion changes, lone lesions and non-consecutive lesion changes, may help radiologists to detect these lesions. This study evaluated a new interpretation revision workflow of lesion annotations in three or more consecutive scans based on these suspicious patterns. MethodsThe interpretation revision workflow was evaluated on manual and computed lesion annotations in longitudinal oncology patient studies. For the manual revision, a senior radiologist and a senior neurosurgeon (the readers) manually annotated the lesions in each scan and later revised their annotations to identify missed and misidentified lesions with the workflow using the automatically detected patterns. For the computerized revision, lesion annotations were first computed with a previously trained nnU-Net and were then automatically revised with an AI-based method that automates the workflow readers’ decisions. The evaluation included 67 patient studies with 2295 metastatic lesions in lung (19 patients, 83 CT scans, 1178 lesions), liver (18 patients, 77 CECT scans, 800 lesions) and brain (30 patients, 102 T1W-Gad MRI scans, 317 lesions). ResultsRevision of the manual lesion annotations revealed 120 missed lesions and 20 misidentified lesions in 31 out of 67 (46%) studies. The automatic revision reduced the number of computed missed lesions by 55 and computed misidentified lesions by 164 in 51 out of 67 (76%) studies. ConclusionAutomatic analysis of three or more consecutive volumetric scans helps find missed and misidentified lesions and may improve the evaluation of temporal changes of oncological lesions.

Improved accuracy of spectral-domain optical coherence tomography and optical coherence tomography angiography for monitoring myopic macular neovascularisation activity

Background/aimsTo evaluate the diagnostic accuracy of spectral-domain optical coherence tomography (SD OCT) combined with OCT angiography (OCTA) for myopic myopic macular neovascularisation (MNV) activity.MethodsBoth eyes of patients with myopic MNV...

An attentional and working memory theory of hallucination vulnerability in frontotemporal dementia

Abstract The rate and prevalence of hallucinations in behavioural variant frontotemporal dementia is well established. The mechanisms for underlying vulnerability however are the least well described in FTD compared with other neuropsychiatric conditions, despite the presence of these features significantly complicating the diagnostic process. As such, this present study aimed to provide a detailed characterization of the neural, cognitive and behavioural profile associated with a predisposition to hallucinatory experiences in behavioural variant frontotemporal dementia. In total, 153 patients with behavioural variant frontotemporal dementia were recruited sequentially for this study. A group of patients with well characterized hallucinations and good-quality volumetric MRI scans (n = 23) were genetically and demographically matched to a group without hallucinations (n = 23) and a healthy control cohort (n = 23). All patients were assessed at their initial visit by means of a detailed clinical interview, a comprehensive battery of neuropsychological tests and MRI. Data were analysed according to three levels: (i) the relationship between neural structures, cognition, behaviour and hallucinations in behavioural variant frontotemporal dementia; (ii) the impact of the C9orf72 expansion; and (iii) hallucination subtype on expression of hallucinations. Basic and complex attentional (including divided attention and working memory) and visual function measures differed between groups (all P &amp;lt; 0.001) with hallucinators demonstrating poorer performance, along with evidence of structural changes centred on the prefrontal cortex, caudate and cerebellum (corrected for False Discovery Rate at P &amp;lt; 0.05 with a cluster threshold of 100 contiguous voxels). Attentional processes were also implicated in C9orf72 carriers with hallucinations with structural changes selectively involving the thalamus. Patients with visual hallucinations in isolation showed a similar pattern with emphasis on cerebellar atrophy. Our findings provided novel insights that attentional and visual function subsystems and related distributed brain structures are implicated in the generation of hallucinations in behavioural variant frontotemporal dementia, that dissociate across C9orf72, sporadic behavioural variant frontotemporal dementia and for the visual subtype of hallucinations. This loading on attentional and working memory measures is in line with current mechanistic models of hallucinations that frequently suggest a failure of integration of cognitive and perceptual processes. We therefore propose a novel cognitive and neural model for hallucination predisposition in behavioural variant frontotemporal dementia that aligns with a transdiagnostic model for hallucinations across neurodegeneration and psychiatry.

Rediscovering Age-Related Macular Degeneration with Swept-Source OCT Imaging: The 2022 Charles L. Schepens, MD, Lecture

Swept-source OCT angiography (SS-OCTA) scans of eyes with age-related macular degeneration (AMD) were used to replace color, autofluorescence, infrared reflectance, and dye-based fundus angiographic imaging for the diagnosis and staging of AMD. Through the use of different algorithms with the SS-OCTA scans, both structural and angiographic information can be viewed and assessed using both cross sectional and en face imaging strategies. Presented at the 2022 Charles L. Schepens, MD, Lecture at the American Academy of Ophthalmology Retina Subspecialty Day, Chicago, Illinois, on September 30,2022. Patients with AMD. Review of published literature and ongoing clinical research using SS-OCTA imaging in AMD. Swept-source OCT angiography imaging of AMD at different stages of disease progression. Volumetric SS-OCTA dense raster scans were used to diagnose and stage both exudative and nonexudative AMD. In eyes with nonexudative AMD, a single SS-OCTA scan was used to detect and measure structural features in the macula such as the area and volume of both typical soft drusen and calcified drusen, the presence and location of hyperreflective foci, the presence of reticular pseudodrusen, also known as subretinal drusenoid deposits, the thickness of the outer retinal layer, the presence and thickness of basal laminar deposits, the presence and area of persistent choroidal hypertransmission defects, and the presence of treatment-naïve nonexudative macular neovascularization. In eyes with exudative AMD, the same SS-OCTA scan pattern was used to detect and measure the presence of macular fluid, the presence and type of macular neovascularization, and the response of exudation to treatment with vascular endothelial growth factor inhibitors. In addition, the same scan pattern was used to quantitate choriocapillaris (CC) perfusion, CC thickness, choroidal thickness, and the vascularity of the choroid. Compared with using several different instruments to perform multimodal imaging, a single SS-OCTA scan provides a convenient, comfortable, and comprehensive approach for obtaining qualitative and quantitative anatomic and angiographic information to monitor the onset, progression, and response to therapies in both nonexudative and exudative AMD. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Using spatiotemporal information in weather radar data to detect and track communal roosts

AbstractThe exodus of flying animals from their roosting locations is often visible as expanding ring‐shaped patterns in weather radar data. The NEXRAD network, for example, archives more than 25 years of data across 143 contiguous US radar stations, providing opportunities to study roosting locations and times and the ecosystems of birds and bats. However, access to this information is limited by the cost of manually annotating millions of radar scans. We develop and deploy an AI‐assisted system to annotate roosts in radar data. We build datasets with roost annotations to support the training and evaluation of automated detection models. Roosts are detected, tracked, and incorporated into our developed web‐based interface for human screening to produce research‐grade annotations. We deploy the system to collect swallow and martin roost information from 12 radar stations around the Great Lakes spanning 21 years. After verifying the practical value of the system, we propose to improve the detector by incorporating both spatial and temporal channels from volumetric radar scans. The deployment on Great Lakes radar scans allows accelerated annotation of 15 628 roost signatures in 612 786 radar scans with 183.6 human screening hours, or 1.08 s per radar scan. We estimate that the deployed system reduces human annotation time by ~7×. The temporal detector model improves the average precision at intersection‐over‐union threshold 0.5 (APIoU = .50) by 8% over the previous model (48%→56%), further reducing human screening time by 2.3× in its pilot deployment. These data contain critical information about phenology and population trends of swallows and martins, aerial insectivore species experiencing acute declines, and have enabled novel research. We present error analyses, lay the groundwork for continent‐scale historical investigation about these species, and provide a starting point for automating the detection of other family‐specific phenomena in radar data, such as bat roosts and mayfly hatches.

Predicting the Outcome of First Episode Psychosis Subjects by Assessing Dorsolateral Prefrontal Cortex Volume

Aim: Dorsolateral prefrontal cortex (DLPFC), Neurological Soft Signs (NSS) and Cognitive impairment have been described as predictors of outcome of First Episode Psychosis (FEP), therefore we aimed to find the predictors of clinical, social and functional outcome variables in a cohort of first episode non affective psychotic subjects. Subjects and Methods: A prospective follow up study was conducted from August 2018 to August 2020 in a tertiary care hospital of South India. A semi-structured questionnaire was given to all subjects for socio-demographic details. All subjects were assessed with Heidelberg scale, Bender Gestalt Test (BGT) and underwent MRI Brain 3D volumetric scan to examine NSS, cognitive impairment, and DLPFC volume at baseline respectively. Brief Psychiatric Rating Scale (BPRS) and Social and Occupational Functioning Assessment Scale scales (SOFAS) were administered at baseline, 1 month and at three month follow up. At 3 months, clinical and socio-functional outcome was defined by BPRS scores and SOFAS scores. Pearson’s correlation was found between DLPFC volume of all subjects, BPRS, BGT scores at baseline with BPRS and SOFAS at 3 months. To test the statistical significance of the comparison of mean values of all continuous clinical and demographic parameters between two groups of BPRS and SOFAS, Mann Whitney U test was used. Results: Smaller DLPFC volume predicted clinical, socio-functional outcome significantly. A significant moderate correlation was found between NSS and BPRS scores at baseline. Conclusion: Baseline right DLPFC volume can be an important predictor of clinical and socio-functional outcome in FEP.

Characterizing Low-cost Registration for Photographic Images to Computed Tomography.

Mapping information from photographic images to volumetric medical imaging scans is essential for linking spaces with physical environments, such as in image-guided surgery. Current methods of accurate photographic image to computed tomography (CT) image mapping can be computationally intensive and/or require specialized hardware. For general purpose 3-D mapping of bulk specimens in histological processing, a cost-effective solution is necessary. Here, we compare the integration of a commercial 3-D camera and cell phone imaging with a surface registration pipeline. Using surgical implants and chuck-eye steak as phantom tests, we obtain 3-D CT reconstruction and sets of photographic images from two sources: Canfield Imaging's H1 camera and an iPhone 14 Pro. We perform surface reconstruction from the photographic images using commercial tools and open-source code for Neural Radiance Fields (NeRF) respectively. We complete surface registration of the reconstructed surfaces with the iterative closest point (ICP) method. Manually placed landmarks were identified at three locations on each of the surfaces. Registration of the Canfield surfaces for three objects yields landmark distance errors of 1.747, 3.932, and 1.692 mm, while registration of the respective iPhone camera surfaces yields errors of 1.222, 2.061, and 5.155 mm. Photographic imaging of an organ sample prior to tissue sectioning provides a low-cost alternative to establish correspondence between histological samples and 3-D anatomical samples.