Cone-beam Computed Tomography Datasets Research Articles

Publicly Available Dental Image Datasets for Artificial Intelligence.

The development of artificial intelligence (AI) in dentistry requires large and well-annotated datasets. However, the availability of public dental imaging datasets remains unclear. This study aimed to provide a comprehensive overview of all publicly available dental imaging datasets to address this gap and support AI development. This observational study searched all publicly available dataset resources (academic databases, preprints, and AI challenges), focusing on datasets/articles from 2020 to 2023, with PubMed searches extending back to 2011. We comprehensively searched for dental AI datasets containing images (intraoral photos, scans, radiographs, etc.) using relevant keywords. We included datasets of >50 images obtained from publicly available sources. We extracted dataset characteristics, patient demographics, country of origin, dataset size, ethical clearance, image details, FAIRness metrics, and metadata completeness. We screened 131,028 records and extracted 16 unique dental imaging datasets. The datasets were obtained from Kaggle (18.8%), GitHub, Google, Mendeley, PubMed, Zenodo (each 12.5%), Grand-Challenge, OSF, and arXiv (each 6.25%). The primary focus was tooth segmentation (62.5%) and labeling (56.2%). Panoramic radiography was the most common imaging modality (58.8%). Of the 13 countries, China contributed the most images (2,413). Of the datasets, 75% contained annotations, whereas the methods used to establish labels were often unclear and inconsistent. Only 31.2% of the datasets reported ethical approval, and 56.25% did not specify a license. Most data were obtained from dental clinics (50%). Intraoral radiographs had the highest findability score in the FAIR assessment, whereas cone-beam computed tomography datasets scored the lowest in all categories. These findings revealed a scarcity of publicly available imaging dental data and inconsistent metadata reporting. To promote the development of robust, equitable, and generalizable AI tools for dental diagnostics, treatment, and research, efforts are needed to address data scarcity, increase diversity, mandate metadata completeness, and ensure FAIRness in AI dental imaging research.

The relationship between radiomorphometric indices and fractal dimension analysis: a cone-beam computed tomography study.

The purpose of this study is to evaluate the relationship between radiomorphometric indices and fractal dimension (FD) analysis in jawbone radiographic density using Cone-Beam Computed Tomography (CBCT). The study included 87 CBCT datasets from dental patients aged 50-79. Four radiomorphometric indices (Computed Tomography Cortical Index (CTCI), Computed Tomography Mental Index (CTMI), Computed Tomography Index-Superior (CTI-S), and Computed Tomography Index-Inferior (CTI-I)) along with age, gender, remaining teeth, and mandibular bone height were evaluated. FD was assessed in trabecular bone near the mental foramen (ROI 1) and on the mandibular cortical bone (ROI 2). Relationships between indices and variables were analyzed using Pearson chi-square, Fisher's exact, one-way ANOVA, Kruskal-Wallis, and Spearman's rho tests. ANOVA showed significant differences in mandibular bone height (p = .004) and FD at ROI 2 (p = .000) across three mandibular cortex types (C1, C2, C3). Spearman's rho indicated significant correlations between FD at ROI 2 and CTMI, CTI-S, CTI-I (p = .000), and between mandibular bone height and CTMI (p = .010). The correlations of FD at ROI 2 ranged from strongest to weakest with CTCI, CTMI, CTI-S, and CTI-I. High-risk osteoporosis radiomorphometric indices are associated with FD analysis of cortical bone.

CRML-Net: Cross-Modal Reasoning and Multi-Task Learning Network for tooth image segmentation

Data from a single modality may suffer from noise, low contrast, or other imaging limitations that affect the model’s accuracy. Furthermore, due to the limited amount of data, most models trained on single-modality data tend to overfit the training set and perform poorly on out-of-domain data. Therefore, in this paper, we propose a network named Cross-Modal Reasoning and Multi-Task Learning Network (CRML-Net), which combines cross-modal reasoning and multi-task learning, aiming to leverage the complementary information between different modalities and tasks to enhance the model’s generalization ability and accuracy. Specifically, CRML-Net consists of two stages. In the first stage, our network extracts a new morphological information modality from the original image and then performs cross-modal fusion with the original modality image, aiming to leverage the morphological information to enhance the model’s robustness to out-of-domain datasets. In the second stage, based on the output of the previous stage, we introduce a multi-task learning mechanism, aiming to improve the model’s performance on unseen data by sharing surface detail information from auxiliary tasks. We validated our method on a publicly available tooth cone beam computed tomography dataset. Our evaluation demonstrates that our method outperforms state-of-the-art approaches.

An Overview of Cone-Beam Computed Tomography and Dental Panoramic Radiography in Dentistry in the Community.

This study reviews the two most important and frequently used systems of tomography used in dentistry today. These are the dental panoramic radiograph (DPR) and cone-beam computed tomography (CBCT). The importance of the DPR has been accentuated by the recent COVID-19 pandemic, as it does not produce an aerosol. Its clinical importance is derived from its panoramic display of the jaws and associated structures and should be examined for incidental findings that may portend a potentially serious outcome. An important recent spin-off of the DPR is the extra-oral bitewing, which can replace its traditional, uncomfortable and aerosol-generating intra-oral counterpart. Although much has been written about them, this paper reviews their essential attributes and limitations in clinical dentistry. Although attempts have been made to reproduce some of the attributes of CT in CBCT such as Hounsfield Units (HU) and improve the contrast resolution of the soft tissues, these remain elusive. Nevertheless, CBCT's dataset should be appropriately reconstructed to fully display the clinical feature prompting its prescription. In certain cases, more than one mode of reconstruction is required.

Evaluation of gingival thickness obtained from intraoral scanning registration with cone beam computed tomography at different acquisition modes

To investigate the reliability of gingival thickness measurements obtained from the registration of intraoral scans with cone beam computed tomography (CBCT) examinations at different acquisition modes. CBCT examinations of 9 porcine hemimandibles were acquired using the OP300 Maxio unit operating at the highest-dose protocol with the smallest voxel size as the reference standard for visualizing the gingival surface. Subsequently, the hemimandibles were surrounded by water to simulate soft tissue attenuation of radiation, and additional CBCT examinations were acquired in 4 modes: Endo, High-resolution, Standard-resolution, and Low-dose. These CBCT datasets were registered with corresponding intraoral scans obtained with the Carestream Dental 3600 intraoral scanning system using the Blue Sky Plan 4 software. Four oral radiologists measured the buccal gingival thickness on cross-sectional reconstructions at 4 measurement sites and 2 distances from the gingival margin in the CBCT reference standard examinations and examinations obtained with water and intraoral scan registration. Multifactorial analysis of variance was used to assess the influence of acquisition mode, measurement site, and distance from the gingival margin on measurements (α = 5%; statistical power = 90%). Buccal gingival thickness measurements obtained via CBCT after registration with intraoral scanning were not significantly influenced by acquisition mode (P = .153) or measurement site (P = .089). Gingival thickness measurements derived from the registration of intraoral scans with CBCT examinations at different acquisition modes appear to be reliable.

Automatic localization of cochlear implant electrodes using cone beam computed tomography images

BackgroundCochlear implants (CI) are implantable medical devices that enable the perception of sounds and the understanding of speech by electrically stimulating the auditory nerve in case of inner ear damage. The stimulation takes place via an array of electrodes surgically inserted in the cochlea. After CI implantation, cone beam computed tomography (CBCT) is used to evaluate the position of the electrodes. Moreover, CBCT is used in research studies to investigate the relationship between the position of the electrodes and the hearing outcome of CI user. In clinical routine, the estimation of the position of the CI electrodes is done manually, which is very time-consuming.ResultsThe aim of this study was to optimize procedures of automatic electrode localization from CBCT data following CI implantation. For this, we analyzed the performance of automatic electrode localization for 150 CBCT data sets of 10 different types of electrode arrays. Our own implementation of the method by Noble and Dawant (Lecture notes in computer science (Including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), Springer, pp 152–159, 2015. https://doi.org/10.1007/978-3-319-24571-3_19) for automated electrode localization served as a benchmark for evaluation. Differences in the detection rate and the localization accuracy across types of electrode arrays were evaluated and errors were classified. Based on this analysis, we developed a strategy to optimize procedures of automatic electrode localization. It was shown that particularly distantly spaced electrodes in combination with a deep insertion can lead to apical–basal confusions in the localization procedure. This confusion prevents electrodes from being detected or assigned correctly, leading to a deterioration in localization accuracy.ConclusionsWe propose an extended cost function for automatic electrode localization methods that prevents double detection of electrodes to avoid apical–basal confusions. This significantly increased the detection rate by 11.15 percent points and improved the overall localization accuracy by 0.53 mm (1.75 voxels). In comparison to other methods, our proposed cost function does not require any prior knowledge about the individual cochlea anatomy.

Orthognathic surgery improves compromised natural head position and pharyngeal airway in patients with Skeletal Class II or III malocclusion.

Natural head position (NHP), pharyngeal airway and maxillofacial growth pattern are correlated. The author's previous studies proved that following surgical correction of Skeletal Class II malocclusion, the over-extended NHP returned upright, and the pharyngeal airway space (PAS) dimension expanded. The present study compares the post-operative change in NHP and PAS after orthognathic surgery in Skeletal Class II and III malocclusion patients. Patients receiving orthognathic procedures to correct Skeletal Class II or III malocclusions were reviewed in this retrospective study. Pre-operative and 6-week post-operative cone-beam computed tomography datasets were collected. Variables representing the craniofacial pattern, the NHP and the PAS were measured three-dimensionally. Post-operative variables were compared with their pre-operative counterparts using either repeat-measure 2-way analysis of variance or Wilcoxon matched-pairs signed rank test. Thirty cases of Skeletal Class II malocclusion and 13 cases of Skeletal Class III malocclusion were collected. Preoperatively, the inter-group differences were significant in craniofacial pattern (68.14 ± 3.552 degree vs. 79.63 ± 2.497 degree, p < .0001) and the NHP (68.77 ± 11.02 degree vs. 82.83 ± 7.738 degree, p = .0002) while not significant in PAS; after surgery, the intergroup differences in craniofacial pattern and the NHP between groups decreased, and the PAS increased in both groups. Orthognathic surgery may improve compromised NHP and increase PAS in Skeletal Class II and III malocclusion patients.

Treatment duration by morphology and location of impacted maxillary canines: A cone-beam computed tomography investigation

This study aimed to identify whether patients with impacted maxillary canines take longer to treat than orthodontic patients without an impacted canine. We also sought to identify factors that are predictive of increased treatment duration in patients with impacted maxillary canines and treated by surgical exposure. A retrospective investigation of 37 patients with an impacted maxillary canine, treated by surgical exposure and fixed appliance therapy, was undertaken. In addition, an age- and sex-matched control group of 39 patients (without impacted canines) was also collected. Patient age, sex, and total treatment duration were recorded. For patients with an impacted canine, patient records and pretreatment cone-beam computed tomography datasets were assessed. Point coordinates identifying relevant landmarks were recorded, and a geometric method was used to calculate variables describing canine location, orientation, and apical morphology. Controlling for age and sex, linear regression identified a statistically significant increase in treatment duration of 46.7 and 41.5 weeks for palatal and labial/midalveolar impacted canines, respectively, vs controls (P<0.002). Age and sex of patients with impacted canines collectively affected treatment duration (P= 0.04), with females of increased age being treated faster than younger males. Rotation of the impacted canine crown had a highly significant effect on treatment duration, with every degree of rotation increasing treatment duration by 0.32 weeks (P<0.001). There was a significant degree of multicollinearity between the other radiographic variables. Collectively, radiographic variables describing canine displacement significantly prolonged treatment duration (P<0.001) and explained 29.8% of the variability in total treatment time. The apical morphology of impacted maxillary canines was significantly associated with increased treatment duration (P= 0.01) and explained 11.3% of the overall treatment variability (P= 0.01). Increased total treatment duration of surgically exposed impacted maxillary canines is associated with increasing mesiopalatal canine crown rotation, worsening displacement, and hooked apical morphology.

PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation

In Cone Beam Computed Tomography (CBCT) images, accurate tooth segmentation is crucial for oral health, providing essential guidance for dental procedures such as implant placement and difficult tooth extractions (impactions). However, due to the lack of a substantial amount of dental data and the complexity of tooth morphology in CBCT images, the task of tooth segmentation faces significant challenges. This may lead to issues such as overfitting and training instability in existing algorithms, resulting in poor model generalization. Ultimately, this may impact the accuracy of segmentation results and could even provide incorrect diagnostic and treatment information. In response to these challenges, we introduce PPA-SAM, an innovative dual-encoder segmentation network that merges the currently popular Segment Anything Model (SAM) with the 3D medical segmentation network, VNet. Through the use of adapters, we achieve parameter reuse and fine-tuning, enhancing the model’s adaptability to specific CBCT datasets. Simultaneously, we utilize a three-layer convolutional network as both a discriminator and a generator for adversarial training. The PPA-SAM model seamlessly integrates the high-precision segmentation performance of convolutional networks with the outstanding generalization capabilities of SAM models, achieving more accurate and robust three-dimensional tooth segmentation in CBCT images. Evaluation of a small CBCT dataset demonstrates that PPA-SAM outperforms other networks in terms of accuracy and robustness, providing a reliable and efficient solution for three-dimensional tooth segmentation in CBCT images. This research has a positive impact on the management of dentofacial conditions from oral implantology to orthognathic surgery, offering dependable technological support for future oral diagnostics and treatment planning.

Impact of cone-beam computed tomography artifacts on dose calculation accuracy for lung cancer.

To implement image-guided adaptive radiotherapy (IGART), many studies investigated dose calculations on cone-beam computed tomography (CBCT). A high HU accuracy is crucial for a high dose calculation accuracy and many imaging sites showed satisfactory results. It has been shown that the dose calculation accuracy for lung cancer lags behind. To examine why the dose calculation accuracy for lung is insufficient, the relative effects of the field-of-view (FOV), breathing motion, and scatter on dose calculation accuracy were studied. A framework was built to simulate CBCT scans for lung cancer patients by forward projecting repeat CT (rCT) scans for two scan geometries: small (SFOV) and medium FOV (MFOV). Breathing motion was modeled by applying a 4D deformation vector field to the mid-position rCT. Scatter was modeled by Monte-Carlo simulations with/without an anti-scatter grid (ASG). Simulated projections were reconstructed using filtered back-projection with/without scatter correction. In case of the SFOV, the CBCT images were patched with the planning CT scan in axial direction. The treatment plan was recalculated on the rCT and simulated CBCT. The mean Hounsfield unit (HU) difference (ΔHUmean), the structural similarity index measure (SSIM), and γ metrics were calculated for the CBCT datasets of various imaging settings. The differences in HU, SSIM and dose calculation accuracy for CBCTs with and without breathing motion were negligible (mean ΔHUmean=6.4vs. 13.7, mean SSIM=0.941vs. 0.957, mean γ (ref=MFOV)=0.75). The SFOV resulted in a lower HU (mean ΔHUmean=-9.2vs. 13.7) and SSIM (mean SSIM=0.912vs. 0.957), and therefore in dose differences compared to the MFOV (mean γ=1.22). Scatter led to considerable discrepancies in all metrics. Adding only the ASG improved the results more than only applying a scatter correction algorithm. Combining ASG and scatter correction algorithm resulted in an even higher dose calculation accuracy. Scatter and FOV are the main contributors to dose inaccuracies and motion has only a minor effect on dose calculation accuracy. Therefore, utilizing an appropriate scatter correction and FOV is important to achieve sufficient dose calculation accuracy to facilitate IGART for lung.

An attenuation field network for dedicated cone beam breast CT with short scan and offset detector geometry

The feasibility of full-scan, offset-detector geometry cone-beam CT has been demonstrated for several clinical applications. For full-scan acquisition with offset-detector geometry, data redundancy from complementary views can be exploited during image reconstruction. Envisioning an upright breast CT system, we propose to acquire short-scan data in conjunction with offset-detector geometry. To tackle the resulting incomplete data, we have developed a self-supervised attenuation field network (AFN). AFN leverages the inherent redundancy of cone-beam CT data through coordinate-based representation and known imaging physics. A trained AFN can query attenuation coefficients using their respective coordinates or synthesize projection data including the missing projections. The AFN was evaluated using clinical cone-beam breast CT datasets (n = 50). While conventional analytical and iterative reconstruction methods failed to reconstruct the incomplete data, AFN reconstruction was not statistically different from the reference reconstruction obtained using full-scan, full-detector data in terms of image noise, image contrast, and the full width at half maximum of calcifications. This study indicates the feasibility of a simultaneous short-scan and offset-detector geometry for dedicated breast CT imaging. The proposed AFN technique can potentially be expanded to other cone-beam CT applications.

Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset-A Validation Study.

The aim of this validation study was to comprehensively evaluate the performance and generalization capability of a deep learning-based periapical lesion detection algorithm on a clinically representative cone-beam computed tomography (CBCT) dataset and test for non-inferiority. The evaluation involved 195 CBCT images of adult upper and lower jaws, where sensitivity and specificity metrics were calculated for all teeth, stratified by jaw, and stratified by tooth type. Furthermore, each lesion was assigned a periapical index score based on its size to enable a score-based evaluation. Non-inferiority tests were conducted with proportions of 90% for sensitivity and 82% for specificity. The algorithm achieved an overall sensitivity of 86.7% and a specificity of 84.3%. The non-inferiority test indicated the rejection of the null hypothesis for specificity but not for sensitivity. However, when excluding lesions with a periapical index score of one (i.e., very small lesions), the sensitivity improved to 90.4%. Despite the challenges posed by the dataset, the algorithm demonstrated promising results. Nevertheless, further improvements are needed to enhance the algorithm's robustness, particularly in detecting very small lesions and the handling of artifacts and outliers commonly encountered in real-world clinical scenarios.

Deep learning in computed tomography super resolution using multi-modality data training.

One of the limitations in leveraging the potential of artificial intelligence in X-ray imaging is the limited availability of annotated training data. As X-ray and CT shares similar imaging physics, one could achieve cross-domain data sharing, so to generate labeled synthetic X-ray images from annotated CT volumes as digitally reconstructed radiographs (DRRs). To account for the lower resolution of CT and the CT-generated DRRs as compared to the real X-ray images, we propose the use of super-resolution (SR) techniques to enhance the CT resolution before DRRgeneration. As spatial resolution can be defined by the modulation transfer function kernel in CT physics, we propose to train a SR network using paired low-resolution (LR) and high-resolution (HR) images by varying the kernel's shape and cutoff frequency. This is different to previous deep learning-based SR techniques on RGB and medical images which focused on refining the sampling grid. Instead of generating LR images by bicubic interpolation, we aim to create realistic multi-detector CT (MDCT) like LR images from HR cone-beam CT (CBCT)scans. We propose and evaluate the use of a SR U-Net for the mapping between LR and HR CBCT image slices. We reconstructed paired LR and HR training volumes from the same CT scans with small in-plane sampling grid size of . We used the residual U-Net architecture to train two models. SRUN : trained with kernel-based LR images, and SRUN : trained with bicubic downsampled data as baseline. Both models are trained on one CBCT dataset (n = 13 391). The performance of both models was then evaluated on unseen kernel-based and interpolation-based LR CBCT images (n = 10 950), and also on MDCT images (n = 1392). Five-fold cross validation and ablation study were performed to find the optimal hyperparameters. Both SRUN and SRUN models show significant improvements (p-value 0.05) in mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and structural similarity index measures (SSIMs) on unseen CBCT images. Also, the improvement percentages in MAE, PSNR, and SSIM by SRUN is larger than SRUN . For SRUN , MAE is reduced by 14%, and PSNR and SSIMs increased by 6 and 8%, respectively. To conclude, SRUN outperforms SRUN , which the former generates sharper images when tested with kernel-based LR CBCT images as well as cross-modality LR MDCTdata. Our proposed method showed better performance than the baseline interpolation approach on unseen LR CBCT. We showed that the frequency behavior of the used data is important for learning the SR features. Additionally, we showed cross-modality resolution improvements to LR MDCT images. Our approach is, therefore, a first and essential step in enabling realistic high spatial resolution CT-generated DRRs for deep learningtraining.

Comparison between observer-based and AI-based reading of CBCT datasets: An interrater-reliability study

ObjectiveTo assess the performance of human observers and convolutional neural networks (CNNs) in detecting periodontal lesions in cone beam computed tomography (CBCT), a total of 38 datasets were examined. Three human readers and a CNN-based solution were employed to evaluate the presence of periodontal pathologies in these datasets. Materials and MethodsDatasets were acquired with a Veraview X800 L P (JMorita Mfg. Corp., Kyoto, Japan). Three general dentists, previously calibrated by a general principal investigator, read the datasets in 3D MPR mode using Horos(LGPL license at Horosproject.org and sponsored by Nimble Co LLC d/b/a Purview in Annapolis, MD, USA) as a DICOM reader. All pathological changes including vertical bone loss, furcation involvement, and periradicular osteolysis were detected. Furthermore, the same datasets were analyzed automatically by Diagnocat (Diagnocat LLC, Prague, Czech Republic), a deep CNN. Finally, the performance of the dentists and the CNN were compared and evaluated. ResultsThe CNN’s performance was significantly lower compared to the human readers in the search for different types of lesions. The human observers achieved good to very good interobserver agreement, except for the evaluation of the vertical lesions, which resulted in a moderate agreement. ConclusionThe CNN used in this study was found to be ineffective in identifying periodontal lesions and was not adequately trained to offer significant assistance in the automated evaluation of periodontal lesions in CBCT datasets.

Accuracy of Palatal Orthodontic Mini-Implants Placed Using Fully Digital Planned Insertion Guides: A Cadaver Study.

Digital workflows have become integral in orthodontic diagnosis and therapy, reducing risk factors and chair time with one-visit protocols. This study assessed the transfer accuracy of fully digital planned insertion guides for orthodontic mini-implants (OMIs) compared with freehanded insertion. Cone-beam computed tomography (CBCT) datasets and intraoral surface scans of 32 cadaver maxillae were used to place 64 miniscrews in the anterior palate. Three groups were formed, two using printed insertion guides (A and B) and one with freehand insertion (C). Group A used commercially available customized surgical templates and Group B in-house planned and fabricated insertion guides. Postoperative CBCT datasets were superimposed with the planning model, and accuracy measurements were performed using orthodontic software. Statistical differences were found for transverse angular deviations (4.81° in A vs. 12.66° in B and 5.02° in C, p = 0.003) and sagittal angular deviations (2.26° in A vs. 2.20° in B and 5.34° in C, p = 0.007). However, accurate insertion depth was not achieved in either guide group; Group A insertion was too shallow (-0.17 mm), whereas Group B insertion was deeper (+0.65 mm) than planned. Outsourcing the planning and fabrication of computer-aided design and computer-aided manufacturing insertion guides may be beneficial for certain indications; particularly, in this study, commercial templates demonstrated superior accuracy than our in-house-fabricated insertion guides.

Offline Verification of Daily Adaptive Therapy for Locally Advanced Lung Cancer

Daily adaptive therapy (AdaptT) for locally advanced lung cancer has been an intriguing field with the adoption of cone beam CT (CBCT). We have utilized AdaptT for lung cancer. In this workflow, contours for targets and organs at risk from the original CT simulation dataset are deformed and registered to the CBCT for each fraction. A physicist reviews and edits the deformed contours online without MD presence and a new plan is treated daily. Treating physicians are required to attend 1 fraction per week. We sought to evaluate the accuracy of these contours for targets and OARs in comparison to a thoracic physician contouring offline with the same CBCT dataset. The impetus of this study was to evaluate the model of physics directed contouring and ensure that gross tumor volume (GTV) misses are not occurring during treatment, defined as Planning Target Volumes (PTV's) derived from AdaptT failing to encompass the GTV redrawn offline by the physician. Six consecutive patients were treated from 2021-2022 with AdaptT utilizing processes as described above. One CBCT per week was re-contoured by a physician for 5-6 CBCT's per case. The GTVs, heart, and lungs were redrawn without registration. The physician was blinded to the structures approved for that fraction and from clinical information outside of the CT simulation data. The clinical target volume expansions were 5 mm shaved from normal tissue barriers and a uniform 5 mm expansion to form the PTV. The Dice Similarity Coefficient (DSE), mean surface distance (MSD), 95th percentile Hausdorff distance (95HD) for contours were calculated and the True Positive Fraction (TPF) of the redrawn GTVs within the treated PTVs were calculated. Thirty CBCTs from 6 patients were contoured. The TPF was 1 for each redrawn GTV, indicating each GTV drawn by the physician offline would have been encompassed within the PTV for treatment. The median 95HF for the targets was below 5 mm (Table 1) and the MSD for targets was close to 1.07 mm. The DSC for Heart and Lungs approached 1.0, indicating close similarity. The average time the physician spent on contouring was 9.8 minutes compared to 9.6 minutes with AdaptT. All redrawn GTVs were within the treated PTV and there was close agreement of normal structures. We evaluated the current model employing daily physics overview with weekly physician input and it appears safe and feasible. Further prospective study is needed to standardize comparison of contour quality and validate clinical outcomes of this approach.

Modular Neural Networks for Osteoporosis Detection in Mandibular Cone-Beam Computed Tomography Scans.

In this technical note, we examine the capabilities of deep convolutional neural networks (DCNNs) for diagnosing osteoporosis through cone-beam computed tomography (CBCT) scans of the mandible. The evaluation was conducted using 188 patients' mandibular CBCT images utilizing DCNN models built on the ResNet-101 framework. We adopted a segmented three-phase method to assess osteoporosis. Stage 1 focused on mandibular bone slice identification, Stage 2 pinpointed the coordinates for mandibular bone cross-sectional views, and Stage 3 computed the mandibular bone's thickness, highlighting osteoporotic variances. The procedure, built using ResNet-101 networks, showcased efficacy in osteoporosis detection using CBCT scans: Stage 1 achieved a remarkable 98.85% training accuracy, Stage 2 minimized L1 loss to a mere 1.02 pixels, and the last stage's bone thickness computation algorithm reported a mean squared error of 0.8377. These findings underline the significant potential of AI in osteoporosis identification and its promise for enhanced medical care. The compartmentalized method endorses a sturdier DCNN training and heightened model transparency. Moreover, the outcomes illustrate the efficacy of a modular transfer learning method for osteoporosis detection, even when relying on limited mandibular CBCT datasets. The methodology given is accompanied by the source code available on GitLab.

Quantitative assessment of adaptive radiotherapy for prostate cancer using deep learning: Bladder dose as a decision criterion.

Adaptive radiotherapy (ART) can incorporate anatomical variations in a reoptimized treatment plan for fractionated radiotherapy. An automatic solution to objectively determine whether ART should be performed immediately after the daily image acquisition is highly desirable. We investigate a quantitative criterion for whether ART should be performed in prostate cancer radiotherapy by synthesizing pseudo-CT (sCT) images and evaluating dosimetric impact on treatment planning using deep learning approaches. Planning CT (pCT) and daily cone-beam CT (CBCT) data sets of 74 patients are used to train (60 patients) and evaluate (14 patients) a cycle adversarial generative network (CycleGAN) that performs the task of synthesizing high-quality sCT from daily CBCT. Automatic delineation (AD) of the bladder is performed on the sCT using the U-net. The combination of sCT and AD allows us to perform dose calculations based on the up-to-date bladder anatomy to determine whether the original treatment plan (ori-plan) is still applicable. For positive cases that the patients' anatomical changes and the associated dose calculations warrant re-planning, we made rapid plan revisions (re-plan) based on the ori-plan. The mean absolute error within the region-of-interests (i.e., body, bladder, fat, muscle) between the sCT and pCT are 41.2, 25.1, 26.5, and 29.0HU, respectively. Taking the calculated results of pCT doses as the standard, for PTV, the gamma passing rates of sCT doses at 1mm/1%, 2mm/2% are 87.92%, 98.78%, respectively. The Dice coefficients of the AD-contours are 0.93 on pCT and 0.91 on sCT. According to the result of dose calculation, we found when the bladder volume underwent a substantial change (79.7%), the bladder dose is still within the safe limit, suggesting it is insufficient to solely use the bladder volume change as a criterion to determine whether adaptive treatment needs to be done. After AD-contours of the bladder using sCT, there are two cases whose bladder dose . For the two cases, we perform re-planning to reduce the bladder dose to , under the condition that the PTV meets the prescribed dose. We provide a dose accurate adaptive workflow for prostate cancer patients by using deep learning approaches, and implement ART that adapts to bladder dose. Of note, the specific replanning criterion for whether ART needs to be performed can adapt to different centers' choices based on their experience and daily observations.

Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset

Preoperative radiological identification of mandibular canals is essential for maxillofacial surgery. This study demonstrates the reproducibility of a deep learning system (DLS) by evaluating its localisation performance on 165 heterogeneous cone beam computed tomography (CBCT) scans from 72 patients in comparison to an experienced radiologist’s annotations. We evaluated the performance of the DLS using the symmetric mean curve distance (SMCD), the average symmetric surface distance (ASSD), and the Dice similarity coefficient (DSC). The reproducibility of the SMCD was assessed using the within-subject coefficient of repeatability (RC). Three other experts rated the diagnostic validity twice using a 0–4 Likert scale. The reproducibility of the Likert scoring was assessed using the repeatability measure (RM). The RC of SMCD was 0.969 mm, the median (interquartile range) SMCD and ASSD were 0.643 (0.186) mm and 0.351 (0.135) mm, respectively, and the mean (standard deviation) DSC was 0.548 (0.138). The DLS performance was most affected by postoperative changes. The RM of the Likert scoring was 0.923 for the radiologist and 0.877 for the DLS. The mean (standard deviation) Likert score was 3.94 (0.27) for the radiologist and 3.84 (0.65) for the DLS. The DLS demonstrated proficient qualitative and quantitative reproducibility, temporal generalisability, and clinical validity.

4D-CT deformable image registration using unsupervised recursive cascaded full-resolution residual networks.

A novel recursive cascaded full-resolution residual network (RCFRR-Net) for abdominal four-dimensional computed tomography (4D-CT) image registration was proposed. The entire network was end-to-end and trained in the unsupervised approach, which meant that the deformation vector field, which presented the ground truth, was not needed during training. The network was designed by cascading three full-resolution residual subnetworks with different architectures. The training loss consisted of the image similarity loss and the deformation vector field regularization loss, which were calculated based on the final warped image and the fixed image, allowing all cascades to be trained jointly and perform the progressive registration cooperatively. Extensive network testing was conducted using diverse datasets, including an internal 4D-CT dataset, a public DIRLAB 4D-CT dataset, and a 4D cone-beam CT (4D-CBCT) dataset. Compared with the iteration-based demon method and two deep learning-based methods (VoxelMorph and recursive cascaded network), the RCFRR-Net achieved consistent and significant gains, which demonstrated that the proposed method had superior performance and generalization capability in medical image registration. The proposed RCFRR-Net was a promising tool for various clinical applications.