A study on the radiomic correlation between CBCT and pCT scans based on modified 3D-RUnet image segmentation
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ABSTRACT:
SUBMITTER: Yu Y
PROVIDER: S-EPMC10921553 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC8293763 | biostudies-literature
Project description:Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarce. To validate the mandibular canal localization accuracy of a deep learning system (DLS) we trained it with 982 CBCT scans and evaluated using 150 scans of five scanners from clinical workflow patients of European and Southeast Asian Institutes, annotated by four radiologists. The interobserver variability was compared to the variability between the DLS and the radiologists. In addition, the generalisation of DLS to CBCT scans from scanners not used in the training data was examined to evaluate its out-of-distribution performance. The DLS had a statistically significant difference (p < 0.001) with lower variability to the radiologists with 0.74 mm than the interobserver variability of 0.77 mm and generalised to new devices with 0.63 mm, 0.67 mm and 0.87 mm (p < 0.001). For the radiologists' consensus segmentation, used as a gold standard, the DLS showed a symmetric mean curve distance of 0.39 mm, which was statistically significantly different (p < 0.001) compared to those of the individual radiologists with values of 0.62 mm, 0.55 mm, 0.47 mm, and 0.42 mm. These results show promise towards integration of DLS into clinical workflow to reduce time-consuming and labour-intensive manual tasks in implantology.
| S-EPMC9633839 | biostudies-literature
Project description:Anatomical segmentation of brain scans is highly relevant for diagnostics and neuroradiology research. Conventionally, segmentation is performed on T 1-weighted MRI scans, due to the strong soft-tissue contrast. In this work, we report on a comparative study of automated, learning-based brain segmentation on various other contrasts of MRI and also computed tomography (CT) scans and investigate the anatomical soft-tissue information contained in these imaging modalities. A large database of in total 853 MRI/CT brain scans enables us to train convolutional neural networks (CNNs) for segmentation. We benchmark the CNN performance on four different imaging modalities and 27 anatomical substructures. For each modality we train a separate CNN based on a common architecture. We find average Dice scores of 86.7 ± 4.1% (T 1-weighted MRI), 81.9 ± 6.7% (fluid-attenuated inversion recovery MRI), 80.8 ± 6.6% (diffusion-weighted MRI) and 80.7 ± 8.2% (CT), respectively. The performance is assessed relative to labels obtained using the widely-adopted FreeSurfer software package. The segmentation pipeline uses dropout sampling to identify corrupted input scans or low-quality segmentations. Full segmentation of 3D volumes with more than 2 million voxels requires <1s of processing time on a graphical processing unit.
| S-EPMC8318570 | biostudies-literature
Project description:This study examines the effect of four different image segmentation methods on the precision of the data. Keywords: other
2004-03-16 | GSE1054 | GEO
Project description:A patient's position changes in every CBCT scan despite patient alignment protocols. However, there have been studies to determine image quality differences when an object is located at the center of the field of view (FOV). To evaluate changes in the image quality of the CBCT scan according to different object positions, the image quality indexes of the Alphard 3030 (Alphard Roentgen Ind., Ltd., Kyoto, Japan) and the Rayscan Symphony (RAY Ind., Ltd., Suwon, Korea) were measured using the Quart DVT_AP phantom at the center of the FOV and 6 peripheral positions under four types of exposure conditions. Anterior, posterior, right, left, upper, and lower positions 1 cm offset from the center of the FOV were used for the peripheral positions. We evaluated and compared the voxel size, homogeneity, contrast to noise ratio (CNR), and the 10% point of the modulation transfer function (MTF10%) of the center and periphery. Because the voxel size, which is determined by the Nyquist frequency, was within tolerance, other image quality indexes were not influenced by the voxel size. For the CNR, homogeneity, and MTF10%, there were peripheral positions which showed considerable differences with statistical significance. The average difference between the center and periphery was up to 31.27% (CNR), 70.49% (homogeneity), and 13.64% (MTF10%). Homogeneity was under tolerance at some of the peripheral locations. Because the CNR, homogeneity, and MTF10% were significantly affected by positional changes of the phantom, an object's position can influence the interpretation of follow up CBCT images. Therefore, efforts to locate the object in the same position are important.
| S-EPMC4836729 | biostudies-literature
Project description:BackgroundSpatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ. Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity of image resolution along orthogonal directions due to acquisition constraints; as a result, they have not been widely used in practice.MethodsTo address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology.ResultsWe evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics.ConclusionsThe proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.
| S-EPMC10724925 | biostudies-literature
Project description:SummaryThis application note describes a new scalable semi-automatic approach, the Dual Point Decision Process, for segmentation of 3D structures contained in 3D microscopy. The segmentation problem is distributed to many individual workers such that each receives only simple questions regarding whether two points in an image are placed on the same object. A large pool of micro-labor workers available through Amazon's Mechanical Turk system provides the labor in a scalable manner.Availability and implementationPython-based code for non-commercial use and test data are available in the source archive at https://sites.google.com/site/imagecrowdseg/.Contactrgiuly@ucsd.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3654713 | biostudies-literature
Project description:This systematic review aims to identify the available semi-automatic and fully automatic algorithms for inferior alveolar canal localization as well as to present their diagnostic accuracy. Articles related to inferior alveolar nerve/canal localization using methods based on artificial intelligence (semi-automated and fully automated) were collected electronically from five different databases (PubMed, Medline, Web of Science, Cochrane, and Scopus). Two independent reviewers screened the titles and abstracts of the collected data, stored in EndnoteX7, against the inclusion criteria. Afterward, the included articles have been critically appraised to assess the quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Seven studies were included following the deduplication and screening against exclusion criteria of the 990 initially collected articles. In total, 1288 human cone-beam computed tomography (CBCT) scans were investigated for inferior alveolar canal localization using different algorithms and compared to the results obtained from manual tracing executed by experts in the field. The reported values for diagnostic accuracy of the used algorithms were extracted. A wide range of testing measures was implemented in the analyzed studies, while some of the expected indexes were still missing in the results. Future studies should consider the new artificial intelligence guidelines to ensure proper methodology, reporting, results, and validation.
| S-EPMC8744855 | biostudies-literature
Project description:Transrectal ultrasound (TRUS) imaging is clinically used in prostate biopsy and therapy. Segmentation of the prostate on TRUS images has many applications. In this study, a three-dimensional (3D) segmentation method for TRUS images of the prostate is presented for 3D ultrasound-guided biopsy.This segmentation method utilizes a statistical shape, texture information, and intensity profiles. A set of wavelet support vector machines (W-SVMs) is applied to the images at various subregions of the prostate. The W-SVMs are trained to adaptively capture the features of the ultrasound images in order to differentiate the prostate and nonprostate tissue. This method consists of a set of wavelet transforms for extraction of prostate texture features and a kernel-based support vector machine to classify the textures. The voxels around the surface of the prostate are labeled in sagittal, coronal, and transverse planes. The weight functions are defined for each labeled voxel on each plane and on the model at each region. In the 3D segmentation procedure, the intensity profiles around the boundary between the tentatively labeled prostate and nonprostate tissue are compared to the prostate model. Consequently, the surfaces are modified based on the model intensity profiles. The segmented prostate is updated and compared to the shape model. These two steps are repeated until they converge. Manual segmentation of the prostate serves as the gold standard and a variety of methods are used to evaluate the performance of the segmentation method.The results from 40 TRUS image volumes of 20 patients show that the Dice overlap ratio is 90.3% ± 2.3% and that the sensitivity is 87.7% ± 4.9%.The proposed method provides a useful tool in our 3D ultrasound image-guided prostate biopsy and can also be applied to other applications in the prostate.
| S-EPMC3360689 | biostudies-other
Project description:The purpose of this study is to evaluate and compare image quality characteristics for two commonly used and commercially available CBCT systems: the X-ray Volumetric Imager and the On-Board Imager. A commonly used CATPHAN image quality phantom was used to measure various image quality parameters, namely, pixel value stability and accuracy, noise, contrast to noise ratio (CNR), high-contrast resolution, low contrast resolution and image uniformity. For the XVI unit, we evaluated the image quality for four manufacturer-supplied protocols as a function of mAs. For the OBI unit, we did the same for the full-fan and half-fan scanning modes, which were respectively used with the full bow-tie and half bow-tie filters. For XVI, the mean pixel values of regions of interest were found to generally decrease with increasing mAs for all protocols, while they were relatively stable with mAs for OBI. Noise was slightly lower on XVI and was seen to decrease with increasing mAs, while CNR increased with mAs for both systems. For XVI and OBI, the high-contrast resolution was approximately limited by the pixel resolution of the reconstructed image. On OBI images, up to 6 and 5 discs of 1% and 0.5% contrast, respectively, were visible for a high mAs setting using the full-fan mode, while none of the discs were clearly visible on the XVI images for various mAs settings when the medium resolution reconstruction was used. In conclusion, image quality parameters for XVI and OBI have been quantified and compared for clinical protocols under various mAs settings. These results need to be viewed in the context of a recent study that reported the dose-mAs relationship for the two systems and found that OBI generally delivered higher imaging doses than XVI.
| S-EPMC5718664 | biostudies-literature