Dataset Information

In vivo microvascular network imaging of the human retina combined with an automatic three-dimensional segmentation method.

ABSTRACT: Microvascular network of the retina plays an important role in diagnosis and monitoring of various retinal diseases. We propose a three-dimensional (3-D) segmentation method with intensity-based Doppler variance (IBDV) based on swept-source optical coherence tomography. The automatic 3-D segmentation method is used to obtain seven surfaces of intraretinal layers. The microvascular network of the retina, which is acquired by the IBDV method, can be divided into six layers. The microvascular network of the six individual layers is visualized, and the morphology and contrast images can be improved by using the segmentation method. This method has potential for earlier diagnosis and precise monitoring in retinal vascular diseases.

SUBMITTER: Huang S

PROVIDER: S-EPMC4572094 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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In vivo microvascular network imaging of the human retina combined with an automatic three-dimensional segmentation method.

Huang Shenghai S Piao Zhonglie Z Zhu Jiang J Lu Fan F Chen Zhongping Z

Journal of biomedical optics 20150701 7

Microvascular network of the retina plays an important role in diagnosis and monitoring of various retinal diseases. We propose a three-dimensional (3-D) segmentation method with intensity-based Doppler variance (IBDV) based on swept-source optical coherence tomography. The automatic 3-D segmentation method is used to obtain seven surfaces of intraretinal layers. The microvascular network of the retina, which is acquired by the IBDV method, can be divided into six layers. The microvascular netwo ...[more]

PMID: 26169790

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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.

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.

Dataset Information

In vivo microvascular network imaging of the human retina combined with an automatic three-dimensional segmentation method.

Publications

In vivo microvascular network imaging of the human retina combined with an automatic three-dimensional segmentation method.

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