In vivo microvascular network imaging of the human retina combined with an automatic three-dimensional segmentation method.
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ABSTRACT:
SUBMITTER: Huang S
PROVIDER: S-EPMC4572094 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundParameters obtained from two-dimensional (2D) cross-sectional images have been used to determine body composition. However, data from three-dimensional (3D) volumetric body images reflect real body composition more accurately and may be better predictors of patient outcomes in cancer. This study aimed to assess the 3D parameters and determine the best predictive factors for patient prognosis.MethodsPatients who underwent surgery for colorectal cancer (CRC) between 2010 and 2016 were included in this study. Preoperative computed tomography images were analysed using an automatic segmentation program. Body composition parameters for muscle, muscle adiposity, subcutaneous fat (SF) and abdominal visceral fat (AVF) were assessed using 2D images at the third lumbar (L3) level and 3D images of the abdominal waist (L1-L5). The cut-off points for each parameter were determined using X-tile software. A Cox proportional hazards regression model was used to identify the association between the parameters and the treatment outcomes, and the relative influence of each parameter was compared using a gradient boosting model.ResultsOverall, 499 patients were included in the study. At a median follow-up of 59 months, higher 3D parameters of the abdominal muscles and SF from the abdominal waist were found to be associated with longer overall survival (OS) and disease-free survival (all P < 0.001). Although the 3D parameters of AVF were not related to survival outcomes, patients with a high AVF volume and mass experienced higher rate of postoperative complications than those with low AVF volume (27.4% vs. 18.7%, P = 0.021, for mass; 27.1% vs. 19.0%, P = 0.028, for volume). Low muscle mass and volume (hazard ratio [HR] 1.959, P = 0.016; HR 2.093, P = 0.036, respectively) and low SF mass and volume (HR 1.968, P = 0.008; HR 2.561, P = 0.003, respectively), both in the abdominal waist, were identified as independent prognostic factors for worse OS. Along with muscle mass and volume, SF mass and volume in the abdominal waist were negatively correlated with mortality (all P < 0.001). Both AVF mass and volume in the abdominal waist were positively correlated with postoperative complications (P < 0.05); 3D muscle volume and SF at the abdominal waist were the most influential factors for OS.Conclusions3D volumetric parameters generated using an automatic segmentation program showed higher correlations with the short- and long-term outcomes of patients with CRC than conventional 2D parameters.
| S-EPMC10834310 | biostudies-literature
Project description:Three-dimensional (3D) cellular-resolution imaging of the living human retina over a large field of view will bring a great impact in clinical ophthalmology, potentially finding new biomarkers for early diagnosis and improving the pathophysiological understanding of ocular diseases. While hardware-based and computational adaptive optics (AO) optical coherence tomography (OCT) have been developed to achieve cellular-resolution retinal imaging, these approaches support limited 3D imaging fields, and their high cost and intrinsic hardware complexity limit their practical utility. Here, this work demonstrates 3D depth-invariant cellular-resolution imaging of the living human retina over a 3 × 3 mm field of view using the first intrinsically phase-stable multi-MHz retinal swept-source OCT and novel computational defocus and aberration correction methods. Single-acquisition imaging of photoreceptor cells, retinal nerve fiber layer, and retinal capillaries is presented across unprecedented imaging fields. By providing wide-field 3D cellular-resolution imaging in the human retina using a standard point-scan architecture routinely used in the clinic, this platform proposes a strategy for expanded utilization of high-resolution retinal imaging in both research and clinical settings.
| S-EPMC10023497 | biostudies-literature
Project description:BackgroundEndometrial thickness is an essential factor affecting female fertility. Clinically, ultrasound imaging is the first choice for the examination of uterine and endometrial-related diseases. However, the boundary of some endometrial is challenging to distinguish due to the effects of image resolution and noise. In addition, the irregular shape of the endometrium makes it more difficult for doctors to measure its thickness. Through the automatic segmentation and extraction of the endometrium, the maximum thickness of the endometrium can be measured automatically and accurately. This provides a quantitative index for doctors to use diagnostically.MethodsIn this study, 85 cases of three-dimensional transvaginal ultrasound (3D TVUS) images were collected retrospectively, including 75 cases of endometrial adhesion and 10 cases of non-adhesion. Firstly, the ultrasound images were filtered by block-matching and 3D filtering and speckle reducing anisotropic diffusion (SRAD). These two kinds of filtered images were combined with the original image to construct a three-channel image. Then, the augmented images were sent to 3D U-Net to realize endometrium segmentation. The performance of the segmentation models was evaluated using the Dice similarity coefficient (DSC), Jaccard, sensitivity, and 95th percentile Hausdorff distance (HD95). Finally, the medial axis transform was used to extract the endometrial centerline, based on which the endometrial thickness could be automatically measured.ResultsThe endometrium segmentation method proposed in this paper achieved 90.83% in Dice, 83.35% in Jaccard, 90.85% in sensitivity, and 12.75 mm in HD95 in the testing set. Taking the doctor's manual measurement as the gold standard, 94.20% of the automatic endometrial thickness measurements based on the segmentation results were within the allowable error range of clinical diagnosis.ConclusionsThis paper presents an automatic endometrium segmentation and thickness measurement method for 3D TVUS images. The experimental results show that this method has high segmentation accuracy to recognize endometrial adhesion images. Furthermore, the thickness measurement based on the segmentation results has high reliability and repeatability, and the accuracy can meet clinical diagnosis needs.
| S-EPMC9338380 | biostudies-literature
Project description:Fetal craniofacial abnormalities are challenging to detect and diagnose on prenatal ultrasound (US). Image segmentation and computer analysis of three-dimensional US volumes of the fetal face may provide an objective measure to quantify fetal facial features and identify abnormalities. We have developed and tested an atlas-based partially automated facial segmentation algorithm; however, the volumes require additional manual segmentation (MS), which is time and labour intensive and may preclude this method from clinical adoption. These manually refined segmentations can then be used as a reference (atlas) by the partially automated segmentation algorithm to improve algorithmic performance with the aim of eliminating the need for manual refinement and developing a fully automated system. This study assesses the inter- and intra-operator variability of MS and tests an optimized version of our automatic segmentation (AS) algorithm. The manual refinements of 15 fetal faces performed by three operators and repeated by one operator were assessed by Dice score, average symmetrical surface distance and volume difference. The performance of the partially automatic algorithm with difference size atlases was evaluated by Dice score and computational time. Assessment of the manual refinements showed low inter- and intra-operator variability demonstrating its suitability for optimizing the AS algorithm. The algorithm showed improved performance following an increase in the atlas size in turn reducing the need for manual refinement.
| S-EPMC7735327 | biostudies-literature
Project description:The aim of this study was to explore whether rotational three-dimensional (3D) visualization of optical coherence tomography angiography (OCTA) volume data may yield valuable information regarding diabetic retinal microaneurysm (MA) characteristics. In this retrospective, observational study, we collected data from 20 patients (20 eyes) with diabetic retinopathy. Subjects were imaged with the SS-OCTA system (PLEX Elite 9000, Carl Zeiss Meditec Inc., Dublin, CA, USA). The OCTA volume data were processed with a volume projection removal algorithm and then exported to imageJ in order to obtain a 3D visualization of the analyzed MAs. The rotational three-dimensional OCTA images were qualitatively and quantitatively investigated. A total of 52 MAs were included in the analysis. On rotational 3D OCTA images, the number of vessels associated with each MA varied between 1 and 4, and most MAs (59.6%) were associated with 2 vessels. Moreover, in 20 MAs (38.4%) these vessels seem to originate from the SCP, while 26 MAs (50.0%) had associated vessels originating from the DVC, and 6 MAs had associated vessels arising from both the SCP and DVC (11.6%). Most MAs (31/52) had a 'saccular' shape. The number of retinal layers occupied by each MA ranged between 1 and 3 and the inner nuclear layer was the retinal layer most frequently occupied by MAs. In conclusion, this study used an algorithm to obtain rotational three-dimensional visualization of retinal MAs. The MAs' architecture is complex and 3D visualization may clarify the true vascular origin of these lesions, which is often mistaken using en face OCTA images.
| S-EPMC6856183 | biostudies-literature
Project description:ObjectiveThe objective is to present a proof-of-concept of a semi-automatic method to reduce hippocampus segmentation time on magnetic resonance images (MRI).Materials and methodsFAst Segmentation Through SURface Fairing (FASTSURF) is based on a surface fairing technique which reconstructs the hippocampus from sparse delineations. To validate FASTSURF, simulations were performed in which sparse delineations extracted from full manual segmentations served as input. On three different datasets with different diagnostic groups, FASTSURF hippocampi were compared to the original segmentations using Jaccard overlap indices and percentage volume differences (PVD). In one data set for which back-to-back scans were available, unbiased estimates of overlap and PVD were obtained. Using longitudinal scans, we compared hippocampal atrophy rates measured by manual, FASTSURF and two automatic segmentations (FreeSurfer and FSL-FIRST).ResultsWith only seven input contours, FASTSURF yielded mean Jaccard indices ranging from 72(±4.3)% to 83(±2.6)% and PVDs ranging from 0.02(±2.40)% to 3.2(±3.40)% across the three datasets. Slightly poorer results were obtained for the unbiased analysis, but the performance was still considerably better than both tested automatic methods with only five contours.ConclusionsFASTSURF segmentations have high accuracy and require only a fraction of the delineation effort of fully manual segmentation. Atrophy rate quantification based on completely manual segmentation is well reproduced by FASTSURF. Therefore, FASTSURF is a promising tool to be implemented in clinical workflow, provided a future prospective validation confirms our findings.
| S-EPMC6338359 | biostudies-literature
Project description:Optical clearing is a versatile approach to improve imaging quality and depth of optical microscopy by reducing scattered light. However, conventional optical clearing methods are restricted in the efficiency-first applications due to unsatisfied time consumption, irreversible tissue deformation, and fluorescence quenching. Here, we developed an ultrafast optical clearing method (FOCM) with simple protocols and common reagents to overcome these limitations. The results show that FOCM can rapidly clarify 300-µm-thick brain slices within 2 min. Besides, the tissue linear expansion can be well controlled by only a 2.12% increase, meanwhile the fluorescence signals of GFP can be preserved up to 86% even after 11 d. By using FOCM, we successfully built the detailed 3D nerve cells model and showed the connection between neuron, astrocyte, and blood vessel. When applied to 3D imaging analysis, we found that the foot shock and morphine stimulation induced distinct c-fos pattern in the paraventricular nucleus of the hypothalamus (PVH). Therefore, FOCM has the potential to be a widely used sample mounting media for biological optical imaging.
| S-EPMC6561250 | biostudies-literature
Project description:To propose a nearly automated left ventricular (LV) three-dimensional (3D) surface segmentation procedure, based on active shape modelling (ASM) and built on a database of 3D echocardiographic (3DE) LV surfaces, for cardiac magnetic resonance (CMR) images, and to test its accuracy for LV volumes computation compared with 'gold standard' manual tracings and discs-summation method.The ASM was created based on segmented LV surfaces (4D LV analysis, Tomtec) from 3DE datasets of 205 patients. Then, it was applied to the cardiac magnetic resonance imaging short-axis (SAX) images stack of 12 consecutive patients. After proper realignment using two- and four-chambers CMR long-axis views both as reference and for initializing LV apex and base (six points in total), the ASM was iteratively and automatically updated to match the information of all the SAX planes contemporaneously, resulting in an endocardial LV 3D mesh from which volume was directly derived. The same CMR images were analysed by an experienced cardiologist to derive end-diastolic and end-systolic volumes. Linear correlation and Bland-Altman analyses were applied vs. the manual 'gold standard'. Active shape modelling results showed high correlations with manual values both for LV volumes (r(2) > 0.98) and ejection fraction (EF) (r(2) > 0.90), non-significant biases and narrow limits of agreement.The proposed method resulted in accurate detection of 3D LV endocardial surfaces, which lead to fast and reliable measurements of LV volumes and EF when compared with manual tracing of CMR SAX images. The segmented 3D mesh, including a realistic LV apex and base, could constitute a novel starting point for more realistic patient-specific finite element modelling.
| S-EPMC4401081 | biostudies-other
Project description:ObjectiveTo present and validate a semi-automatic segmentation protocol to enable an accurate 3D reconstruction of the mandibular condyles using cone beam computed tomography (CBCT).Materials and methodsApproval from the regional medical ethics review board was obtained for this study. Bilateral mandibular condyles in ten CBCT datasets of patients were segmented using the currently proposed semi-automatic segmentation protocol. This segmentation protocol combined 3D region-growing and local thresholding algorithms. The segmentation of a total of twenty condyles was performed by two observers. The Dice-coefficient and distance map calculations were used to evaluate the accuracy and reproducibility of the segmented and 3D rendered condyles.ResultsThe mean inter-observer Dice-coefficient was 0.98 (range [0.95-0.99]). An average 90th percentile distance of 0.32 mm was found, indicating an excellent inter-observer similarity of the segmented and 3D rendered condyles. No systematic errors were observed in the currently proposed segmentation protocol.ConclusionThe novel semi-automated segmentation protocol is an accurate and reproducible tool to segment and render condyles in 3D. The implementation of this protocol in the clinical practice allows the CBCT to be used as an imaging modality for the quantitative analysis of condylar morphology.
| S-EPMC4234209 | biostudies-literature
Project description:Intravascular optical coherence tomography (IV-OCT) is an imaging modality that can be used for the assessment of intracoronary stents. Recent publications pointed to the fact that 3D visualizations have potential advantages compared to conventional 2D representations. However, 3D imaging still requires a time consuming manual procedure not suitable for on-line application during coronary interventions. We propose an algorithm for a rapid and fully automatic 3D visualization of IV-OCT pullbacks. IV-OCT images are first processed for the segmentation of the different structures. This also allows for automatic pullback calibration. Then, according to the segmentation results, different structures are depicted with different colors to visualize the vessel wall, the stent and the guide-wire in details. Final 3D rendering results are obtained through the use of a commercial 3D DICOM viewer. Manual analysis was used as ground-truth for the validation of the segmentation algorithms. A correlation value of 0.99 and good limits of agreement (Bland Altman statistics) were found over 250 images randomly extracted from 25 in vivo pullbacks. Moreover, 3D rendering was compared to angiography, pictures of deployed stents made available by the manufacturers and to conventional 2D imaging corroborating visualization results. Computational time for the visualization of an entire data sets resulted to be ~74 sec. The proposed method allows for the on-line use of 3D IV-OCT during percutaneous coronary interventions, potentially allowing treatments optimization.
| S-EPMC3521298 | biostudies-literature