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Journal of Dental Research Aug 2021Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and...
Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to segment the jaw or the teeth, there is still a lack of fully automated segmentation methods that can simultaneously segment both anatomic structures in CBCT scans (i.e., multiclass segmentation). In this study, we aimed to train and validate a mixed-scale dense (MS-D) convolutional neural network for multiclass segmentation of the jaw, the teeth, and the background in CBCT scans. Thirty CBCT scans were obtained from patients who had undergone orthodontic treatment. Gold standard segmentation labels were manually created by 4 dentists. As a benchmark, we also evaluated MS-D networks that segmented the jaw or the teeth (i.e., binary segmentation). All segmented CBCT scans were converted to virtual 3-dimensional (3D) models. The segmentation performance of all trained MS-D networks was assessed by the Dice similarity coefficient and surface deviation. The CBCT scans segmented by the MS-D network demonstrated a large overlap with the gold standard segmentations (Dice similarity coefficient: 0.934 ± 0.019, jaw; 0.945 ± 0.021, teeth). The MS-D network-based 3D models of the jaw and the teeth showed minor surface deviations when compared with the corresponding gold standard 3D models (0.390 ± 0.093 mm, jaw; 0.204 ± 0.061 mm, teeth). The MS-D network took approximately 25 s to segment 1 CBCT scan, whereas manual segmentation took about 5 h. This study showed that multiclass segmentation of jaw and teeth was accurate and its performance was comparable to binary segmentation. The MS-D network trained for multiclass segmentation would therefore make patient-specific orthodontic treatment more feasible by strongly reducing the time required to segment multiple anatomic structures in CBCT scans.
Topics: Cone-Beam Computed Tomography; Deep Learning; Humans; Image Processing, Computer-Assisted; Neural Networks, Computer; Orthodontics; Spiral Cone-Beam Computed Tomography
PubMed: 33783247
DOI: 10.1177/00220345211005338 -
The International Journal of... 2017The vertebrate head characteristically exhibits a complex pattern with sense organs, brain, paired eyes and jaw muscles, and the brain case is not found in other... (Review)
Review
The vertebrate head characteristically exhibits a complex pattern with sense organs, brain, paired eyes and jaw muscles, and the brain case is not found in other chordates. How the extant vertebrate head has evolved remains enigmatic. Historically, there have been two conflicting views on the origin of the vertebrate head, segmental and non-segmental views. According to the segmentalists, the vertebrate head is organized as a metameric structure composed of segments equivalent to those in the trunk; a metamere in the vertebrate head was assumed to consist of a somite, a branchial arch and a set of cranial nerves, considering that the head evolved from rostral segments of amphioxus-like ancestral vertebrates. Non-segmentalists, however, considered that the vertebrate head was not segmental. In that case, the ancestral state of the vertebrate head may be non-segmented, and rostral segments in amphioxus might have been secondarily gained, or extant vertebrates might have evolved through radical modifications of amphioxus-like ancestral vertebrate head. Comparative studies of mesodermal development in amphioxus and vertebrate gastrula embryos have revealed that mesodermal gene expressions become segregated into two domains anteroposteriorly to specify the head mesoderm and trunk mesoderm only in vertebrates; in this segregation, key genes such as delta and hairy, involved in segment formation, are expressed in the trunk mesoderm, but not in the head mesoderm, strongly suggesting that the head mesoderm of extant vertebrates is not segmented. Taken together, the above finding possibly adds a new insight into the origin of the vertebrate head; the vertebrate head mesoderm would have evolved through an anteroposterior polarization of the paraxial mesoderm if the ancestral vertebrate had been amphioxus-like.
Topics: Animals; Body Patterning; Cephalochordata; Gene Expression Regulation, Developmental; Head; Lancelets; Models, Biological; Somites; Vertebrates
PubMed: 29319111
DOI: 10.1387/ijdb.170121to -
Methods in Molecular Biology (Clifton,... 2022Reverse genetics systems provide a powerful tool to generate recombinant arenavirus expressing reporters to facilitate the investigation of the arenavirus life cycle and...
Reverse genetics systems provide a powerful tool to generate recombinant arenavirus expressing reporters to facilitate the investigation of the arenavirus life cycle and also for the discovery of antiviral countermeasures. The plasmid-encoded viral ribonucleoprotein components initiate the transcription and replication of a plasmid-driven full-length viral genome, resulting in infectious virus. Thereby, this approach is ideal for the generation of recombinant arenaviruses expressing reporter genes that can be used as valid surrogates for virus replication. By splitting the small viral segment (S) into two viral segments (S1 and S2), each of them encoding a reporter gene, recombinant tri-segmented arenavirus can be rescued. Bi-reporter-expressing recombinant tri-segmented arenaviruses represent an excellent tool to study the biology of arenaviruses, including the identification and characterization of both prophylactic and therapeutic countermeasures for the treatment of arenaviral infections. In this chapter, we describe a detailed protocol on the generation and in vitro characterization of recombinant arenaviruses containing a tri-segment genome expressing two reporter genes based on the prototype member in the family, lymphocytic choriomeningitis virus (LCMV). Similar experimental approaches can be used for the generation of bi-reporter-expressing tri-segment recombinant viruses for other members in the arenavirus family.
Topics: Arenaviridae Infections; Genes, Reporter; Humans; Lymphocytic choriomeningitis virus; Reverse Genetics; Virus Replication
PubMed: 35821475
DOI: 10.1007/978-1-0716-2453-1_17 -
The American Journal of Managed Care Feb 2022Empiric segmentation is a rapidly growing, learning health system approach that uses large health care system data sets to identify groups of high-risk patients who may... (Review)
Review
OBJECTIVES
Empiric segmentation is a rapidly growing, learning health system approach that uses large health care system data sets to identify groups of high-risk patients who may benefit from similar interventions. We aimed to review studies that used data-driven approaches to segment high-risk patient populations and describe how their designs and findings can inform health care leaders who are interested in applying similar techniques to their patient populations.
STUDY DESIGN
Structured literature review.
METHODS
We searched for original research articles published since 2000 that identified high-risk adult patient populations and applied data-driven analyses to segment the population. Two reviewers independently extracted study population source and criteria for high-risk designation, segmentation method, data types included, model selection criteria, and model results from the identified studies.
RESULTS
Our search identified 224 articles, 12 of which met criteria for full review. Of these, 8 segmented high-risk patients and 4 segmented diagnoses without assigning patients to unique groups. Studies segmenting patients more often had clinically interpretable results. Common groups were defined by high prevalence of diabetes, cardiovascular disease, psychiatric conditions including substance use disorders, and neurologic disease (eg, stroke). Few studies incorporated patients' functional or social factors. Resulting patient and diagnosis clusters varied in ways closely linked to the model inputs, patient population inclusion criteria, and health care system context.
CONCLUSIONS
Empiric segmentation can yield clinically relevant groups of patients with complex medical needs. Segmentation results are context dependent, suggesting the need for careful design and interpretation of segmentation models to ensure that results can inform clinical care and program design in the target setting.
Topics: Adult; Delivery of Health Care; Humans; Mental Disorders; Research Design
PubMed: 35139299
DOI: 10.37765/ajmc.2022.88752 -
The Lancet. Digital Health Sep 2022Artificial intelligence (AI) and deep learning have shown great potential in streamlining clinical tasks. However, most studies remain confined to in silico validation... (Observational Study)
Observational Study
BACKGROUND
Artificial intelligence (AI) and deep learning have shown great potential in streamlining clinical tasks. However, most studies remain confined to in silico validation in small internal cohorts, without external validation or data on real-world clinical utility. We developed a strategy for the clinical validation of deep learning models for segmenting primary non-small-cell lung cancer (NSCLC) tumours and involved lymph nodes in CT images, which is a time-intensive step in radiation treatment planning, with large variability among experts.
METHODS
In this observational study, CT images and segmentations were collected from eight internal and external sources from the USA, the Netherlands, Canada, and China, with patients from the Maastro and Harvard-RT1 datasets used for model discovery (segmented by a single expert). Validation consisted of interobserver and intraobserver benchmarking, primary validation, functional validation, and end-user testing on the following datasets: multi-delineation, Harvard-RT1, Harvard-RT2, RTOG-0617, NSCLC-radiogenomics, Lung-PET-CT-Dx, RIDER, and thorax phantom. Primary validation consisted of stepwise testing on increasingly external datasets using measures of overlap including volumetric dice (VD) and surface dice (SD). Functional validation explored dosimetric effect, model failure modes, test-retest stability, and accuracy. End-user testing with eight experts assessed automated segmentations in a simulated clinical setting.
FINDINGS
We included 2208 patients imaged between 2001 and 2015, with 787 patients used for model discovery and 1421 for model validation, including 28 patients for end-user testing. Models showed an improvement over the interobserver benchmark (multi-delineation dataset; VD 0·91 [IQR 0·83-0·92], p=0·0062; SD 0·86 [0·71-0·91], p=0·0005), and were within the intraobserver benchmark. For primary validation, AI performance on internal Harvard-RT1 data (segmented by the same expert who segmented the discovery data) was VD 0·83 (IQR 0·76-0·88) and SD 0·79 (0·68-0·88), within the interobserver benchmark. Performance on internal Harvard-RT2 data segmented by other experts was VD 0·70 (0·56-0·80) and SD 0·50 (0·34-0·71). Performance on RTOG-0617 clinical trial data was VD 0·71 (0·60-0·81) and SD 0·47 (0·35-0·59), with similar results on diagnostic radiology datasets NSCLC-radiogenomics and Lung-PET-CT-Dx. Despite these geometric overlap results, models yielded target volumes with equivalent radiation dose coverage to those of experts. We also found non-significant differences between de novo expert and AI-assisted segmentations. AI assistance led to a 65% reduction in segmentation time (5·4 min; p<0·0001) and a 32% reduction in interobserver variability (SD; p=0·013).
INTERPRETATION
We present a clinical validation strategy for AI models. We found that in silico geometric segmentation metrics might not correlate with clinical utility of the models. Experts' segmentation style and preference might affect model performance.
FUNDING
US National Institutes of Health and EU European Research Council.
Topics: Algorithms; Artificial Intelligence; Carcinoma, Non-Small-Cell Lung; Deep Learning; Humans; Lung Neoplasms; Positron Emission Tomography Computed Tomography; United States
PubMed: 36028289
DOI: 10.1016/S2589-7500(22)00129-7 -
Journal of Dentistry May 2023Quantitative analysis of the volume and shape of the temporomandibular joint (TMJ) using cone-beam computed tomography (CBCT) requires accurate segmentation of the...
OBJECTIVE
Quantitative analysis of the volume and shape of the temporomandibular joint (TMJ) using cone-beam computed tomography (CBCT) requires accurate segmentation of the mandibular condyles and the glenoid fossae. This study aimed to develop and validate an automated segmentation tool based on a deep learning algorithm for accurate 3D reconstruction of the TMJ.
MATERIALS AND METHODS
A three-step deep-learning approach based on a 3D U-net was developed to segment the condyles and glenoid fossae on CBCT datasets. Three 3D U-Nets were utilized for region of interest (ROI) determination, bone segmentation, and TMJ classification. The AI-based algorithm was trained and validated on 154 manually segmented CBCT images. Two independent observers and the AI algorithm segmented the TMJs of a test set of 8 CBCTs. The time required for the segmentation and accuracy metrics (intersection of union, DICE, etc.) was calculated to quantify the degree of similarity between the manual segmentations (ground truth) and the performances of the AI models.
RESULTS
The AI segmentation achieved an intersection over union (IoU) of 0.955 and 0.935 for the condyles and glenoid fossa, respectively. The IoU of the two independent observers for manual condyle segmentation were 0.895 and 0.928, respectively (p<0.05). The mean time required for the AI segmentation was 3.6 s (SD 0.9), whereas the two observers needed 378.9 s (SD 204.9) and 571.6 s (SD 257.4), respectively (p<0.001).
CONCLUSION
The AI-based automated segmentation tool segmented the mandibular condyles and glenoid fossae with high accuracy, speed, and consistency. Potential limited robustness and generalizability are risks that cannot be ruled out, as the algorithms were trained on scans from orthognathic surgery patients derived from just one type of CBCT scanner.
CLINICAL SIGNIFICANCE
The incorporation of the AI-based segmentation tool into diagnostic software could facilitate 3D qualitative and quantitative analysis of TMJs in a clinical setting, particularly for the diagnosis of TMJ disorders and longitudinal follow-up.
Topics: Humans; Deep Learning; Temporomandibular Joint; Mandibular Condyle; Temporomandibular Joint Disorders; Cone-Beam Computed Tomography; Image Processing, Computer-Assisted
PubMed: 36870441
DOI: 10.1016/j.jdent.2023.104475 -
BMC Oral Health Sep 2023Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments,...
OBJECTIVE
Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manual teeth segmentation, the gold standard up until now, is time-consuming, tedious, and observer-dependent. This study aims to develop an automated teeth segmentation and labeling system using deep learning.
MATERIAL AND METHODS
As a reference, 1750 OS were manually segmented and labeled. A deep-learning approach based on PointCNN and 3D U-net in combination with a rule-based heuristic algorithm and a combinatorial search algorithm was trained and validated on 1400 OS. Subsequently, the trained algorithm was applied to a test set consisting of 350 OS. The intersection over union (IoU), as a measure of accuracy, was calculated to quantify the degree of similarity between the annotated ground truth and the model predictions.
RESULTS
The model achieved accurate teeth segmentations with a mean IoU score of 0.915. The FDI labels of the teeth were predicted with a mean accuracy of 0.894. The optical inspection showed excellent position agreements between the automatically and manually segmented teeth components. Minor flaws were mostly seen at the edges.
CONCLUSION
The proposed method forms a promising foundation for time-effective and observer-independent teeth segmentation and labeling on intra-oral scans.
CLINICAL SIGNIFICANCE
Deep learning may assist clinicians in virtual treatment planning in orthodontics, prosthetics, implantology, and orthognathic surgery. The impact of using such models in clinical practice should be explored.
Topics: Humans; Deep Learning; Algorithms; Calcium Sulfate; Dental Care; Physical Examination
PubMed: 37670290
DOI: 10.1186/s12903-023-03362-8 -
Scientific Reports Aug 2021High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on...
High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.
PubMed: 34362967
DOI: 10.1038/s41598-021-95480-y -
Frontiers in Cell and Developmental... 2022Wnt signaling pathways are recognized for having major roles in tissue patterning and cell proliferation. In the last years, remarkable progress has been made in... (Review)
Review
Wnt signaling pathways are recognized for having major roles in tissue patterning and cell proliferation. In the last years, remarkable progress has been made in elucidating the molecular and cellular mechanisms that underlie sequential segmentation and axial elongation in various arthropods, and the canonical Wnt pathway has emerged as an essential factor in these processes. Here we review, with a comparative perspective, the current evidence concerning the participation of this pathway during posterior growth, its degree of conservation among the different subphyla within Arthropoda and its relationship with the rest of the gene regulatory network involved. Furthermore, we discuss how this signaling pathway could regulate segmentation to establish this repetitive pattern and, at the same time, probably modulate different cellular processes precisely coupled to axial elongation. Based on the information collected, we suggest that this pathway plays an organizing role in the formation of the body segments through the regulation of the dynamic expression of segmentation genes, controlling the gene, at the posterior region of the embryo/larva, that is necessary for the correct sequential formation of body segments in most arthropods and possibly in their common segmented ancestor. On the other hand, there is insufficient evidence to link this pathway to axial elongation by controlling its main cellular processes, such as convergent extension and cell proliferation. However, conclusions are premature until more studies incorporating diverse arthropods are carried out.
PubMed: 35990604
DOI: 10.3389/fcell.2022.944673 -
Journal of Clinical and Diagnostic... Feb 2017The Couinaud's liver segmentation is based on the identification of portal vein bifurcation and origin of hepatic veins. It is widely used clinically, because it is... (Review)
Review
The Couinaud's liver segmentation is based on the identification of portal vein bifurcation and origin of hepatic veins. It is widely used clinically, because it is better suited for surgery and is more accurate in localizing and monitoring various intra parenchymal lesions. According to standard anatomy, the portal vein bifurcates into right and left branches; the left vein drains segment II, III and IV and the right vein divides into two secondary branches - the anterior portal vein drains segments V and VIII, and the posterior drains segments VI and VII. The portal vein variants such as portal trifurcation, with division of the main portal vein into the left, right anterior, and posterior branches, and the early origin of the right posterior branch directly from the main portal vein were found to be more frequent and was seen in about 20 - 35% of the population. Accurate knowledge of the portal variants and consequent variations in vascular segments are essential for intervention radiologists and transplant surgeons in the proper diagnosis during radiological investigations and in therapeutic applications such as preparation for biopsy, Portal Vein Embolization (PVE), Transjugular Intrahepatic Porto-Systemic Shunt (TIPS), tumour resection and partial hepatectomy for split or living donor transplantations. The advances in the knowledge will reduce intra and postoperative complications and avoid major catastrophic events. The purpose of the present review is to update the normal and variant portal venous anatomy and their implications in the liver segmentations, complex liver surgeries and various radiological intervention procedures.
PubMed: 28384848
DOI: 10.7860/JCDR/2017/25028.9453