Cyst Segmentation Research Articles

Automated segmentation of liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease

Liver and liver cyst volume measurements are important quantitative imaging biomarkers for assessment of disease progression in autosomal dominant polycystic kidney disease (ADPKD) and polycystic liver disease (PLD). To date, no study has presented automated segmentation and volumetric computation of liver and liver cysts in these populations. In this paper, we proposed an automated segmentation framework for liver and liver cysts from bounded abdominal MR images in patients with ADPKD. To model the shape and variations in ADPKD livers, the spatial prior probability map (SPPM) of liver location and the tissue prior probability maps (TPPMs) of liver parenchymal tissue intensity and cyst morphology were generated. Formulated within a three-dimensional level set framework, the TPPMs successfully captured liver parenchymal tissues and cysts, while the SPPM globally constrained the initial surfaces of the liver into the desired boundary. Liver cysts were extracted by combined operations of the TPPMs, thresholding, and false positive reduction based on spatial prior knowledge of kidney cysts and distance map. With cross-validation for the liver segmentation, the agreement between the radiology expert and the proposed method was 84% for shape congruence and 91% for volume measurement assessed by the intra-class correlation coefficient (ICC). For the liver cyst segmentation, the agreement between the reference method and the proposed method was ICC = 0.91 for cyst volumes and ICC = 0.94 for % cyst-to-liver volume.

Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform.

Optical Coherence Tomography (OCT) has emerged as a major diagnostic modality for retinal imaging. Although OCT generates gross volumetric data, manual analysis of the images for locating or quantifying retinal cysts is a time consuming process. Recently semi- and fully-automatic methods for locating and segmenting retinal cysts have been proposed in the literature. Our paper proposes a fully automatic method for intra-retinal cyst segmentation using marker controlled watershed transform on B-scan images obtained on OCT scanning. Markers are obtained using k-means clustering and used as sources for topographical based watershed transform for final segmentation. Proposed method was evaluated both quantitatively and qualitatively on Optima Cyst Challenge dataset against ground truth obtained from two graders. Experimental results show that the proposed method outperformed other recently proposed methods. Our algorithm achieved a recall rate of 82% while preserving precision rate of 77%, and gave a higher correlation rate of 96% with ground truth obtained from two graders.

Segmentation of liver cyst in ultrasound image based on adaptive threshold algorithm and particle swarm optimization

To find the optimum threshold of an image is still an important research topic in the recent years. This paper presents a segmentation of liver cyst for ultrasound image through combining Wellner's thresholding algorithm with particle swarm optimization (PSO). The proposed method firstly obtains an optimal parameter, which expressed as a percentage or fixed amount of dark objects against a white background in a gray image, of Wellner's thresholding algorithm by PSO method. And then the gray image is binarized according to the optimized parameter. Finally, a semi-automatic method for locating and identifying multiple liver cysts or single liver cyst of ultrasound images is performed. For a validation, the results of the proposed technique are compared with those of other segmented methods. We also tested 92 ultrasound images of the liver cysts by our software. The corrected identification rate of the single liver cysts is 97.7 %, and that of multiple liver cysts is 87.5 %. Experimental results demonstrate that the proposed technique is reliable on segmenting the contour of liver cyst and identifying single or multiple liver cysts.

Automatic segmentation of maxillofacial cysts in cone beam CT images

Accurate segmentation of cysts and tumors is an essential step for diagnosis, monitoring and planning therapeutic intervention. This task is usually done manually, however manual identification and segmentation is tedious. In this paper, an automatic method based on asymmetry analysis is proposed which is general enough to segment various types of jaw cysts.The key observation underlying this approach is that normal head and face structure is roughly symmetric with respect to midsagittal plane: the left part and the right part can be divided equally by an axis of symmetry. Cysts and tumors typically disturb this symmetry. The proposed approach consists of three main steps as follows: At first, diffusion filtering is used for preprocessing and symmetric axis is detected. Then, each image is divided into two parts. In the second stage, free form deformation (FFD) is used to correct slight displacement of corresponding pixels of the left part and a reflected copy of the right part. In the final stage, intensity differences are analyzed and a number of constraints are enforced to remove false positive regions.The proposed method has been validated on 97 Cone Beam Computed Tomography (CBCT) sets containing various jaw cysts which were collected from various image acquisition centers. Validation is performed using three similarity indicators (Jaccard index, Dice's coefficient and Hausdorff distance). The mean Dice's coefficient of 0.83, 0.87 and 0.80 is achieved for Radicular, Dentigerous and KCOT classes, respectively. For most of the experiments done, we achieved high true positive (TP). This means that a large number of cyst pixels are correctly classified. Quantitative results of automatic segmentation show that the proposed method is more effective than one of the recent methods in the literature.

A novel benchmark model for intelligent annotation of spectral-domain optical coherence tomography scans using the example of cyst annotation

Background and objectivesThe lack of benchmark data in computational ophthalmology contributes to the challenging task of applying disease assessment and evaluate performance of machine learning based methods on retinal spectral domain optical coherence tomography (SD-OCT) scans. Presented here is a general framework for constructing a benchmark dataset for retinal image processing tasks such as cyst, vessel, and subretinal fluid segmentation and as a result, a benchmark dataset for cyst segmentation has been developed. MethodFirst, a dataset captured by different SD-OCT vendors with different numbers of scans and pathology qualities are selected. Then a robust and intelligent method is used to evaluate performance of readers, partitioning the dataset into subsets. Subsets are then assigned to complementary readers for annotation with respect to a novel confidence based annotation protocol. Finally, reader annotations are combined based on their performance to generate final annotations. ResultThe generated benchmark dataset for cyst segmentation comprises 26 SD-OCT scans with differing cyst qualities, collected from 4 different SD-OCT vendors to cover a wide variety of data. The dataset is partitioned into three subsets which are annotated by complementary readers based on a confidence based annotation protocol. Experimental results show annotations of complementary readers are combined efficiently with respect to their performance, generating accurate annotations. ConclusionOur results facilitate the process of generating benchmark datasets. Moreover the generated benchmark data set for cyst segmentation can be used reliably to train and test machine learning based methods.

Three-dimensional segmentation of retinal cysts from spectral-domain optical coherence tomography images by the use of three-dimensional curvelet based K-SVD

This paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinal cysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3D spectral-domain optical coherence tomography (SD-OCT) images. In particular, we focus on the Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) system, and show the applicability of our algorithm in the segmentation of these features. For this purpose, we use recursive Gaussian filter and approximate the corrupted pixels from its surrounding, then in order to enhance the cystoid dark space regions and future noise suppression we introduce a new scheme in dictionary learning and take curvelet transform of filtered image then denoise and modify each noisy coefficients matrix in each scale with predefined initial 3D sparse dictionary. Dark pixels between retinal pigment epithelium and nerve fiber layer that were extracted with graph theory are considered as cystoid spaces. The average dice coefficient for the segmentation of cystoid regions in whole 3D volume and with-in central 3 mm diameter on the MICCAI 2015 OPTIMA Cyst Segmentation Challenge dataset were found to be 0.65 and 0.77, respectively.

Segmentation of Cysts in Kidney and 3-D Volume Calculation from CT Images

This paper proposes a segmentation method and a three-dimensional (3-D) volume calculation method of cysts in kidney from a number of computer tomography (CT) slice images. The input CT slice images contain both sides of kidneys. There are two segmentation steps used in the proposed method: kidney segmentation and cyst segmentation. For kidney segmentation, kidney regions are segmented from CT slice images by using a graph-cut method that is applied to the middle slice of input CT slice images. Then, the same method is used for the remaining CT slice images. In cyst segmentation, cyst regions are segmented from the kidney regions by using fuzzy C-means clustering and level-set methods that can reduce noise of non-cyst regions. For 3-D volume calculation, cyst volume calculation and 3-D volume visualization are used. In cyst volume calculation, the area of cyst in each CT slice image equals to the number of pixels in the cyst regions multiplied by spatial density of CT slice images, and then the volume of cysts is calculated by multiplying the cyst area and thickness (interval) of CT slice images. In 3-D volume visualization, a 3-D visualization technique is used to show the distribution of cysts in kidneys by using the result of cyst volume calculation. The total 3-D volume is the sum of the calculated cyst volume in each CT slice image. Experimental results show a good performance of 3-D volume calculation. The proposed cyst segmentation and 3-D volume calculation methods can provide practical supports to surgery options and medical practice to medical students.

Development and evaluation of an automatic tumor segmentation tool: A comparison between automatic, semi-automatic and manual segmentation of mandibular odontogenic cysts and tumors

IntroductionIn the treatment of cancer in the head and neck region, computer-assisted surgery can be used to estimate location and extent by segmentation of the tumor. This article presents a new tool (Smartbrush), which allows for faster automated segmentation of the tumor. MethodsThis new method was compared with other well-known techniques of segmentation. Thirty-eight patients with keratocystic odontogenic tumors were included in this study. The tumors were segmented using manual segmentation, threshold-based segmentation and segmentation using Smartbrush. All three methods were compared concerning usability, time expenditure and accuracy. ResultsThe results suggest that segmentation using Smartbrush is significantly faster with comparable accuracy. ConclusionsAfter a period of adjustment to the program, one can comfortably get reliable results that, compared with other methods, are not as dependent on the user's experience. Smartbrush segmentation is a reliable and fast method of segmentation in tumor surgery.

Automated Segmentation of Intraretinal Cystoid Fluid in Optical Coherence Tomography

Cystoid macular edema (CME) is observed in a variety of ocular disorders and is strongly associated with vision loss. Optical coherence tomography (OCT) provides excellent visualization of cystoid fluid, and can assist clinicians in monitoring the progression of CME. Quantitative tools for assessing CME may lead to better metrics for choosing treatment protocols. To address this need, this paper presents a fully automated retinal cyst segmentation technique for OCT image stacks acquired from a commercial scanner. The proposed method includes a computationally fast bilateral filter for speckle denoising while maintaining CME boundaries. The proposed technique was evaluated in images from 16 patients with vitreoretinal disease and three controls. The average sensitivity and specificity for the classification of cystoid regions in CME patients were found to be 91% and 96%, respectively, and the retinal volume occupied by cystoid fluid obtained by the algorithm was found to be accurate within a mean and median volume fraction of 1.9% and 0.8%, respectively.