Segmental Branches Research Articles

The Egyptian pyramid chain was built along the now abandoned Ahramat Nile Branch

The largest pyramid field in Egypt is clustered along a narrow desert strip, yet no convincing explanation as to why these pyramids are concentrated in this specific locality has been given so far. Here we use radar satellite imagery, in conjunction with geophysical data and deep soil coring, to investigate the subsurface structure and sedimentology in the Nile Valley next to these pyramids. We identify segments of a major extinct Nile branch, which we name The Ahramat Branch, running at the foothills of the Western Desert Plateau, where the majority of the pyramids lie. Many of the pyramids, dating to the Old and Middle Kingdoms, have causeways that lead to the branch and terminate with Valley Temples which may have acted as river harbors along it in the past. We suggest that The Ahramat Branch played a role in the monuments’ construction and that it was simultaneously active and used as a transportation waterway for workmen and building materials to the pyramids’ sites.

Predicting enthalpy of formation of benzenoid hydrocarbons and ordering molecular trees using general multiplicative Zagreb indices

Many existing studies show that there exists a strong relationship between structures and characteristics of molecules. Topological indices are often used in modeling the properties of chemical compounds and biological activities in theoretical chemistry. Topological indices are numerical values associated with structures of molecules in such a way that they remain constant under graph isomorphism. Multiplicative Zagreb indices are among the famous topological indices that have been explored by numerous researchers in the last few years. The first objective of the present paper is to examine the importance of general multiplicative Zagreb indices for forecasting the enthalpy of formation of hydrocarbons using a data set of 25 benzenoid hydrocarbons. The second objective of this paper is to study molecular trees with a given order and with a given number of branching vertices or segments using general multiplicative (first and second) Zagreb indices. Sharp lower/upper bounds on these Zagreb indices for the aforementioned molecular trees are obtained and the graphs attaining these bounds are determined. Bounds on the classical multiplicative Zagreb and Narumi-Katayama indices are corollaries of the obtained results.

Transarterial Embolization of Intracranial Dural Arteriovenous Fistulas Cognard Type V With Direct Drainage Into the Perimedullary Veins: Case Report and Literature Review.

Cognard type V fistula (CVF) is a rare type of dural arteriovenous fistula characterized by spinal perimedullary venous drainage. Owing to the lack of pathognomonic findings, misdiagnosis is common. Patients often undergo multiple spinal angiograms negative for spinal vascular malformations. Digital subtraction angiography is the gold standard diagnostic tool. The preferred treatment option is endovascular management with embolization through a transarterial, transvenous, or combined approach. Other options include open surgery, stereotactic radiosurgery, or a combination of both. The patient from case # 1 presented with progressive weakness and hypoesthesia in the bilateral lower extremities, with urinary and bowel incontinence. The DSA identified a CVF fed by the meningohypophyseal trunk and a draining perimedullary vein. Embolization with 0.1 ccs of Onyx-18 was performed with complete fistula occlusion. The patient from case # 2 developed bilateral lower extremity weakness, diffuse numbness, and urinary incontinence. The DSA showed a CVF fed by tributaries from the ascending pharyngeal artery and posterior meningeal artery branches of the V3 segment, draining into a perimedullary vein. Embolization with 0.3 cc of Onyx-18 was performed with 100% occlusion of the fistula. A 1-year follow-up angiogram confirmed complete fistula occlusion. Both patients consented to the procedure. Even if a patient only presents symptoms of myelopathy, CVF should be considered. Herein, we present 2 cases of CVF with direct drainage into the perimedullary veins which presented exclusively with myelopathy syndrome and describe treatment with trasarterial embolization with Onyx.

P191 Giant cell arteritis relapses occur in a different arterial territory as compared to diagnosis

Abstract Background/Aims Ultrasonography (US) for diagnosis of large vessel vasculitis (LVV) is a recommended first line diagnostic procedure. We have been running an US led diagnostic pathway in our centre since January 2017. This pathway is also used to diagnose relapsing disease. We present US defined anatomical distribution of relapses in consecutive patients. Methods Patients in whom new and relapsing disease was diagnosed using US were included. Protocolised US examination of the common carotid system (segments of common carotid, external carotid and branches) and those of the subclavian system (segments from origin of subclavian to end of axillary artery and branches) were carried out at diagnosis and relapse. Halo sign in at least two different arteries was needed for establishing new or relapsing disease. Disease characterisation was classified as being in the carotid system, subclavian system, or both. The nature of the disease at relapse was compared to that at diagnosis. Results 279 patients were diagnosed with LVV using US since Jan 2017. 57 (20%) were diagnosed as having a relapse using US. 43/57 (75%) and 28/57 (49%) had involvement of the carotid system at baseline and relapse respectively. 22/57 (39%) and 44/57 (77%) had involvement of the subclavian system at baseline and relapse respectively. A comparison of the disease involvement is as in Figure 1. Conclusion The branches of the common carotid artery (external carotid artery) are commonly involved at diagnosis, but relapse occurs more frequently in the subclavian artery branches. This is especially true when disease affects the carotid system at baseline. The presentation of patients with subclavian system disease is often with only constitutional symptoms and with shoulder girdle pain and stiffness. This may be mistaken for being a polymyalgia rheumatica relapse. It is essential to examine the subclavian system of arteries in patients with LVV before diagnosing polymyalgia rheumatic relapse. Disclosure S. Alanoor: None. G. Ducker: None. C.B. Mukhtyar: None.

BT-Net: An end-to-end multi-task architecture for brain tumor classification, segmentation, and localization from MRI images

Brain tumors are severe medical conditions that can prove fatal if not detected and treated early. Radiologists often use MRI and CT scan imaging to diagnose brain tumors early. However, a shortage of skilled radiologists to analyze medical images can be problematic in low-resource healthcare settings. To overcome this issue, deep learning-based automatic analysis of medical images can be an effective tool for assistive diagnosis. Conventional methods generally focus on developing specialized algorithms to address a single aspect, such as segmentation, classification, or localization of brain tumors. In this work, a novel multi-task network was proposed, modified from the conventional VGG16, along with a U-Net variant concatenation, that can simultaneously achieve segmentation, classification, and localization using the same architecture. We trained the classification branch using the Brain Tumor MRI Dataset, and the segmentation branch using a “Brain Tumor Segmentation dataset. The integration of our method’s output can aid in simultaneous classification, segmentation, and localization of four types of brain tumors in MRI scans. The proposed multi-task framework achieved 97% accuracy in classification and a dice similarity score of 0.86 for segmentation. In addition, the method shows higher computational efficiency compared to existing methods. Our method can be a promising tool for assistive diagnosis in low-resource healthcare settings where skilled radiologists are scarce.

Automated instance segmentation of asphalt pavement patches based on deep learning

The location and pixel-level information of the patch are all critical data for the quantitative evaluation of pavement conditions. However, obtaining both parch location and pixel-level information simultaneously is a challenge in intelligent pavement patch surveys. This paper proposes a deep-learning-based patch instance segmentation network (PISNet) that employs you only look once (YOLO)v5 as the baseline and adds a semantic segmentation branch to provide an effective solution for this challenge. The proposed PISNet replaces the original backbone CSPDarknet53 and neck of YOLOv5 with a novel feature extractor named symmetrical pyramid network (SPN). The proposed SPN aims at repeating fusion and transfer of shallow semantic features and deep spatial localization features in the order of “FPN-PAN-FPN” such that the multi-scale semantic expression and localization ability of the feature map could be enhanced. Moreover, a modified feature selection module is also incorporated into the SPN as a skip connection to aggregate more spatial details of the feature map while suppressing redundant features. Experimental results show that compared with Mask region convolutional neural network (R-CNN), You only look at coefficients (YOLACT), YOLACT++, EfficientDet, Fully convolutional one stage object detector (FCOS), You only look once version 5m (YOLOv5m), U-Net, DeepLabv3+, and High resolution network-object contextual representations (HRNet-OCR), the proposed PISNet has the best detection performance. Meanwhile, the proposed PISNet achieves superior accuracy/frames per second trade-offs compared to Mask R-CNN, YOLACT, and YOLACT++. Particularly, the proposed PISNet has certain promising potential in supporting pavement patch detection in real-time scenarios and potentially degraded pavement patch detection. Moreover, the proposed PISNet can yield superior segmentation results compared with Mask R-CNN, YOLACT, YOLACT++, U-Net, HRNet-OCR, and DeepLabv3+ on public CRACK500 datasets. Code has been made available at: https://github.com/716HAZ/PISNet .

A Three-branch Jointed Feature and Topology Decoder guided by game-theoretic interactions for temporomandibular joint segmentation

Segmentation of the temporomandibular joint (TMJ) disc and condyle from magnetic resonance imaging (MRI) is a crucial task in TMJ internal derangement research. The automatic segmentation of the disc structure presents challenges due to its intricate and variable shapes, low contrast, and unclear boundaries. Existing TMJ segmentation methods often overlook spatial and channel information in features and neglect overall topological considerations, with few studies exploring the interaction between segmentation and topology preservation. To address these challenges, we propose a Three-Branch Jointed Feature and Topology Decoder (TFTD) for the segmentation of TMJ disc and condyle in MRI. This structure effectively preserves the topological information of the disc structure and enhances features. We introduce a cross-dimensional spatial and channel attention mechanism (SCIA) to enhance features. This mechanism captures spatial, channel, and cross-dimensional information of the decoded features, leading to improved segmentation performance. Moreover, we explore the interaction between topology preservation and segmentation from the perspective of game theory. Based on this interaction, we design the Joint Loss Function (JLF) to fully leverage the features of segmentation, topology preservation, and joint interaction branches. Results on the TMJ MRI dataset demonstrate the superior performance of our TFTD compared to existing methods.

Instance segmentation algorithm for sorting dismantling components of end-of-life vehicles

In the recycling and dismantling procedures of End-of-Life Vehicles, the diverse thicknesses of disassembled parts necessitate specialized recycling processes, underscoring the imperative for efficient classification and recycling methods. Presently, the classification of disassembled components from scrapped vehicles relies heavily on manual visual inspection and caliper measurements, resulting in inefficiency and low accuracy. To overcome the challenges associated with thickness classification for disassembled components of End-of-Life Vehicles, propose a paradigm shift by introducing an intelligent grading strategy. This approach aims to significantly improve efficiency and accuracy in the classification process, offering a more effective solution for the recycling of dismantled parts. In response to the inefficiencies, low accuracy, and safety concerns associated with manual sorting, propose a deep learning model, incorporating an attention mechanism, for accomplishing instance segmentation tasks of End-of-Life Vehicles components. The model introduces a global attention mechanism into the backbone network for feature extraction, which is further integrated into the instance segmentation branch, resulting in a significant enhancement of instance segmentation performance. The model is trained and optimized using a dataset collected from an End-of-Life Vehicles dismantling facility in China. Experimental results show that when the intersection over union is 0.5, the mean Average Precision of the model in target detection and instance segmentation reaches 93.2% and 92.1% respectively., and 77.2% and 72.4% in the intersection over union is 0.5–0.95. In comparison to the unmodified baseline model, the model exhibits improvements of 1.7%, 2%, 5.8%, and 3.1% across evaluation metrics. When contrasted with traditional manual sorting methods, the proposed model exhibits pronounced advantages in terms of accuracy and fairness. In conclusion, the model effectively addresses the challenge of intelligent sorting of dismantled components from End-of-Life Vehicles during the dismantling process.

SRFormer: Text Detection Transformer with Incorporated Segmentation and Regression

Existing techniques for text detection can be broadly classified into two primary groups: segmentation-based and regression-based methods. Segmentation models offer enhanced robustness to font variations but require intricate post-processing, leading to high computational overhead. Regression-based methods undertake instance-aware prediction but face limitations in robustness and data efficiency due to their reliance on high-level representations. In our academic pursuit, we propose SRFormer, a unified DETR-based model with amalgamated Segmentation and Regression, aiming at the synergistic harnessing of the inherent robustness in segmentation representations, along with the straightforward post-processing of instance-level regression. Our empirical analysis indicates that favorable segmentation predictions can be obtained at the initial decoder layers. In light of this, we constrain the incorporation of segmentation branches to the first few decoder layers and employ progressive regression refinement in subsequent layers, achieving performance gains while minimizing computational load from the mask. Furthermore, we propose a Mask-informed Query Enhancement module. We take the segmentation result as a natural soft-ROI to pool and extract robust pixel representations, which are then employed to enhance and diversify instance queries. Extensive experimentation across multiple benchmarks has yielded compelling findings, highlighting our method's exceptional robustness, superior training and data efficiency, as well as its state-of-the-art performance. Our code is available at https://github.com/retsuh-bqw/SRFormer-Text-Det.

DGCBG-Net: A dual-branch network with global cross-modal interaction and boundary guidance for tumor segmentation in PET/CT images

Background and objectivesAutomatic tumor segmentation plays a crucial role in cancer diagnosis and treatment planning. Computed tomography (CT) and positron emission tomography (PET) are extensively employed for their complementary medical information. However, existing methods ignore bilateral cross-modal interaction of global features during feature extraction, and they underutilize multi-stage tumor boundary features. MethodsTo address these limitations, we propose a dual-branch tumor segmentation network based on global cross-modal interaction and boundary guidance in PET/CT images (DGCBG-Net). DGCBG-Net consists of 1) a global cross-modal interaction module that extracts global contextual information from PET/CT images and promotes bilateral cross-modal interaction of global feature; 2) a shared multi-path downsampling module that learns complementary features from PET/CT modalities to mitigate the impact of misleading features and decrease the loss of discriminative features during downsampling; 3) a boundary prior-guided branch that extracts potential boundary features from CT images at multiple stages, assisting the semantic segmentation branch in improving the accuracy of tumor boundary segmentation. ResultsExtensive experiments are conducted on STS and Hecktor 2022 datasets to evaluate the proposed method. The average Dice scores of our DGCB-Net on the two datasets are 80.33% and 79.29%, with average IOU scores of 67.64% and 70.18%. DGCB-Net outperformed the current state-of-the-art methods with a 1.77% higher Dice score and a 2.12% higher IOU score. ConclusionsExtensive experimental results demonstrate that DGCBG-Net outperforms existing segmentation methods, and is competitive to state-of-arts.

SegIns: A simple extension to instance discrimination task for better localization learning

Recent self-supervised learning methods, where instance discrimination task is a fundamental way of pretraining convolutional neural networks (CNN), excel in transfer learning performance. Even though instance discrimination task is a well suited pretraining method for classification with its image-level learning, lack of dense representation learning makes it sub-optimal for localization tasks such as object detection. In this paper, we aim to mitigate this shortcoming of instance discrimination task by extending it to jointly learn dense representations alongside image-level representations. We add a segmentation branch parallel to the image-level learning to predict class-agnostic masks, enhancing location-awareness of the representations. We show the effectiveness of our pretraining approach on localization tasks by transferring the learned representations to object detection and segmentation tasks, providing relative improvements by up to 1.7% AP on PASCAL VOC and 0.8% AP on COCO object detection, 0.8% AP on COCO instance segmentation and 3.6% mIoU on PASCAL VOC semantic segmentation respectively.

Investigation of the anatomical course of articular branches to the thoracic zygapophysial joints

BackgroundRecent studies have questioned the validity of targeting the thoracic medial branch via anesthetic blocks or radiofrequency neurotomy for the diagnosis or treatment of pain from the thoracic zygapophysial joints. PurposeTo define the origin and course of the articular branches to the thoracic zygapophysial joints at all levels. DesignCadaveric dissection. SettingThe Gross Anatomy Laboratory at the University of New England College of Osteopathic Medicine. SubjectsOne cadaveric thoracic spine. MethodsGross and stereoscopic dissection of the bilateral dorsal rami T1–T12 was performed on one embalmed cadaver. The medial and lateral branches were traced from their cutaneous distribution to the origin at the dorsal ramus. The articular branches were identified using stereoscopic dissection by tracing their origin from the dorsal ramus or medial branch to the capsule of the zygapophysial joint. The images were recorded using digital photography. ResultsTwenty-two of the twenty-four articular branches were identified in a single cadaveric specimen. Articular branches at T7 on the right side and T9 on the left side were not identified. 5 of the 22 (18%) articular branches were observed to arise from the proximal segment of the medial branch of the dorsal ramus. Of the remainder, 17 of the 22 (78%) articular branches were observed to arise from the dorsal primary ramus itself. At levels T1-9, the articular branch coursed inferiorly along the lamina to reach the joint capsule. At levels T10-T12, the articular branch traversed the intervertebral foramen to reach the facet joint. The T12 medial branch on the left was seen to travel inferior to innervate the L1/L2 zygapophysial joint. ConclusionThe source of the innervation for the thoracic zygapophysial is variable and can arise from the dorsal ramus itself or from the proximal segment of the medial branch. Unlike the cervical and lumbar medial branches, which innervate two joints each, in this case, the thoracic dorsal rami appear to innervate only the zygapophysial joint at the level of the nerve exit. It should be noted that the findings may be limited in their generalizability due to the reliance on a single cadaver study.

Surgical treatment of intracranial epithelioid hemangioendothelioma: a case report

Epithelioid hemangioendothelioma (EHE) is an extremely rare vascular neoplasm with an intermediate pattern of malignancy between benign neoplasms and angiosarcomas. Only 46 cases of intracranial localization of primary EHE in adults have been published.Case report: A 47-year-old male patient developed pronounced speech disorder of sensory and amnestic aphasia type, right-sided pyramidal hemiparesis within 3–4 days. Magnetic resonance imaging revealed a delimited left sided brain islet neoplasm with signs of hemorrhage and small perifocal edema. Intraoperatively, the neoplasm was represented by a conglomerate of pathologic vessels, with involvement of the terminal branches of the M2 segment of the middle cerebral artery, which prevented radical resection of the neoplasm. Based on histopathological and immunohistochemistry analysis of the neoplasm (positive expression of CD31, CD34, proliferative activity index Ki-67 10%) the morphologic diagnosis of EHE was established. 3 months after surgery, continued growth of the residual part of the neoplasm was noted. Temozolomide chemotherapy was clinically ineffective. The patient died due to the development of dislocation syndrome after 9 months.Discussion. The malignant characteristics of EHE include invasive growth, recurrence, and metastasis, which is more common in intracranial localization of the neoplasm. If EHE is suspected, radical surgical removal of the neoplasm should be sought.

Separate first, then segment: An integrity segmentation network for salient object detection

Current methods aggregate multi-level features or introduce auxiliary information to get more refined saliency maps. However, little attention is paid to how to obtain complete salient objects in cluttered background. To address this problem, we propose an integrity segmentation network (ISNet) with a novel detection paradigm that first separates the targets completely and then segment them finely. Specifically, the ISNet architecture consists of a target separation (TS) branch and an object segmentation (OS) branch, trained using a hierarchical difference-aware (HDA) loss. The TS branch equipped with a fractal structure is utilized to produce saliency features with expanded boundary (SF w/ EB), which can enlarge the difference of border details to separate the target from background completely. Compared with the edge and skeleton information, the SF w/ EB contains a more complete structure, which can supplement the defect of salient objects. The OS branch is leveraged to generate complementary features, which gradually integrates the SF w/ EB and aggregated features to segment complete saliency maps. Moreover, we propose the HDA loss to further improve the structural integrity of prediction, which hierarchically assigns weight to pixels according to their differences. Hard pixels will be given more attention to discriminate the similar parts between foreground and background. Comprehensive experimental results on five datasets show that the proposed ISNet outperforms the state-of-the-art methods both quantitatively and qualitatively. Concretely, compared with three typical models, the average gain percentage reaches 2.6% in terms of Fβ, Sm and MAE on two large complex datasets. The improvements demonstrate that the proposed ISNet are beneficial for improving the integrity of prediction. Besides, the ISNet is efficient and runs at a real-time speed of 39.5 FPS when processing an image with size of 320 × 320. Furthermore, the proposed model has better generalization, which can also be applied to other vision tasks to handle complex scenes. Codes are available at https://github.com/lesonly/ISNet.

Leukemoid Reaction in an Elderly Patient with Aortic Valve Infective Endocarditis and Pancreatic Adenocarcinoma

Leukemoid reaction (LR) associated with solid tumors has been documented for many decades. LR is often associated with an unfavorable prognosis and aggressive course of the disease. However, the differential diagnosis of LR is of significant difficulty when a patient has several potential etiological factors, each of them individually may cause LR or, on the contrary, lead to a systemic reaction of the body within a single pathogenetic chain.We present a clinical observation of an elderly patient admitted to the intensive care unit due to the first-time encountered weakness in the right extremities. Clinical and instrumental examination revealed an acute cerebral ischemia with leukocytosis increase up to 60.000 cells/μL with leukocyte formula left shift and subsequent patient decompensation with lethal outcome, despite the intensive treatment.Autopsy revealed a low-differentiated adenocarcinoma of the pancreatic tail with multiple metastatic lesions in regional lymph nodes and liver, as well as a competing disease — acute infective endocarditis of the aortic valve, which was the cause of sepsis development with septicemia type and thromboembolism both in the great circulation circle with the presence of ischemic cerebral infarction, spleen infarcts, and in the small circle with the development of thromboembolism in the right segmental branches of the pulmonary artery. Given the advanced stage of pancreatic cancer and lack of direct evidence of sepsis at primary diagnosis, paraneoplastic nature of LR is more likely, but infective endocarditis and concomitant pathology also may have contributed to the development of LR.

Long-term day-by-day tracking of microvascular networks sprouting in fibrin gels: From detailed morphological analyses to general growth rules.

Understanding and controlling of the evolution of sprouting vascular networks remains one of the basic challenges in tissue engineering. Previous studies on the vascularization dynamics have typically focused only on the phase of intense growth and often lacked spatial control over the initial cell arrangement. Here, we perform long-term day-by-day analysis of tens of isolated microvasculatures sprouting from endothelial cell-coated spherical beads embedded in an external fibrin gel. We systematically study the topological evolution of the sprouting networks over their whole lifespan, i.e., for at least 14 days. We develop a custom image analysis toolkit and quantify (i) the overall length and area of the sprouts, (ii) the distributions of segment lengths and branching angles, and (iii) the average number of branch generations-a measure of network complexity. We show that higher concentrations of vascular endothelial growth factor (VEGF) lead to earlier sprouting and more branched networks, yet without significantly affecting the speed of growth of individual sprouts. We find that the mean branching angle is weakly dependent on VEGF and typically in the range of 60°-75°, suggesting that, by comparison with the available diffusion-limited growth models, the bifurcating tips tend to follow local VEGF gradients. At high VEGF concentrations, we observe exponential distributions of segment lengths, which signify purely stochastic branching. Our results-due to their high statistical relevance-may serve as a benchmark for predictive models, while our new image analysis toolkit, offering unique features and high speed of operation, could be exploited in future angiogenic drug tests.

Homoeologous exchanges contribute to branch angle variations in rapeseed: Insights from transcriptome, QTL-seq and gene functional analysis.

Branch angle (BA) is a critical morphological trait that significantly influences planting density, light interception and ultimately yield in plants. Despite its importance, the regulatory mechanism governing BA in rapeseed remains poorly understood. In this study, we generated 109 transcriptome data sets for 37 rapeseed accessions with divergent BA phenotypes. Relative to adaxial branch segments, abaxial segments accumulated higher levels of auxin and exhibited lower expression of six TCP1 homologues and one GA20ox3. A co-expression network analysis identified two modules highly correlated with BA. The modules contained homologues to known BA control genes, such as FUL, YUCCA6, TCP1 and SGR3. Notably, a homoeologous exchange (HE), occurring at the telomeres of A09, was prevalent in large BA accessions, while an A02-C02 HE was common in small BA accessions. In their corresponding regions, these HEs explained the formation of hub gene hotspots in the two modules. QTL-seq analysis confirmed that the presence of a large A07-C06 HE (~8.1 Mb) was also associated with a small BA phenotype, and BnaA07.WRKY40.b within it was predicted as candidate gene. Overexpressing BnaA07.WRKY40.b in rapeseed increased BA by up to 20°, while RNAi- and CRISPR-mediated mutants (BnaA07.WRKY40.b and BnaC06.WRKY40.b) exhibited decreased BA by up to 11.4°. BnaA07.WRKY40.b was exclusively localized to the nucleus and exhibited strong expression correlations with many genes related to gravitropism and plant architecture. Taken together, our study highlights the influence of HEs on rapeseed plant architecture and confirms the role of WRKY40 homologues as novel regulators of BA.

Centrilobular emphysema and airway dysanapsis: factors associated with low respiratory function in younger smokers.

Low respiratory function in young adulthood is one of the important factors in the trajectory leading to the future development of COPD, but its morphological characteristics are not well characterised. We retrospectively enrolled 172 subjects aged 40-49 years with ≥10 pack-years smoking history who underwent lung cancer screening by computed tomography (CT) and spirometry at two Japanese hospitals. Emphysema was visually assessed according to the Fleischner Society guidelines and classified into two types: centrilobular emphysema (CLE) and paraseptal emphysema (PSE). Airway dysanapsis was assessed with the airway/lung ratio (ALR), which was calculated by the geometric mean of the lumen diameters of the 14 branching segments divided by the cube root of total lung volume on a CT scan. Among the subjects, CLE and PSE were observed in 20.9% and 30.8%, respectively. The mean ALR was 0.04 and did not differ between those with and without each type of emphysema. Multivariable regression analysis models adjusted for age, sex, body mass index and smoking status indicated that CLE and a low ALR were independently associated with lower forced expiratory volume in 1 s (FEV1)/forced vital capacity (estimate -1.64 (95% CI -2.68- -0.60) and 6.73 (95% CI 4.24-9.24), respectively) and FEV1 % pred (estimate -2.81 (95% CI -5.10- -0.52) and 10.9 (95% CI 5.36-16.4), respectively). CLE and airway dysanapsis on CT were independently associated with low respiratory function in younger smokers.

Towards Fully Autonomous UAV: Damaged Building-Opening Detection for Outdoor-Indoor Transition in Urban Search and Rescue

Autonomous unmanned aerial vehicle (UAV) technology is a promising technology for minimizing human involvement in dangerous activities like urban search and rescue missions (USAR), both in indoor and outdoor. Automated navigation from outdoor to indoor environments is not trivial, as it encompasses the ability of a UAV to automatically map and locate the openings in a damaged building. This study focuses on developing a deep learning model for the detection of damaged building openings in real time. A novel damaged building-opening dataset containing images and mask annotations, as well as a comparison between single and multi-task learning-based detectors are given. The deep learning-based detector used in this study is based on YOLOv5. First, this study compared the different versions of YOLOv5 (i.e., small, medium, and large) capacity to perform damaged building-opening detections. Second, a multitask learning YOLOv5 was trained on the same dataset and compared with the single-task detector. The multitask learning (MTL) was developed based on the YOLOv5 object detection architecture, adding a segmentation branch jointly with the detection head. This study found that the MTL-based YOLOv5 can improve detection performance by combining detection and segmentation losses. The YOLOv5s-MTL trained on the damaged building-opening dataset obtained 0.648 mAP, an increase of 0.167 from the single-task-based network, while its inference speed was 73 frames per second on the tested platform.

Tracheobronchial variations in Pneumoconiosis cases: multidetector computed tomography diagnosis.

Tracheobronchial variations (TBVs) are more common than previously believed due to the increasing use of multi-detector computed tomography (MDCT). This study aimed to assess TBVs in cases of pneumoconiosis, one of the oldest occupational diseases that still poses a threat to public health. This was a descriptive study that involved reviewing chest MDCT images of 34 cases of pneumoconiosis and 34 control cases retrospectively from January 2020 to April 2022. Variations in the trachea, right main bronchus, left main bronchus, lobar and segmental branches of the cases in the patient and control groups were evaluated according to Boyden's nomenclature. The frequency of TBV was 32.4% in pneumoconiosis cases. Although the frequency of TBV was higher in the patient group than in the control group, the difference was not statistically significant (p=0.086). Furthermore, there was no significant difference in terms of TBV classification between the patient and control groups (p=0.407). Additionally, the presence of TBV did not affect the distribution of International Labour Organization categories in pneumoconiosis cases (p=0.360). Although our study provides initial insights into the occurrence of TBVs in pneumoconiosis cases, further research is needed to clarify the relationship between these variations and the disease.