Computerized Segmentation Method Research Articles

Computerized Segmentation Method for Nonmasses on Breast DCE-MRI Images Using ResUNet++ with Slice Sequence Learning and Cross-Phase Convolution.

The purpose of this study was to develop a computerized segmentation method for nonmasses using ResUNet++ with a slice sequence learning and cross-phase convolution to analyze temporal information in breast dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) images. The dataset consisted of a series of DCE-MRI examinations from 54 patients, each containing three-phase images, which included one image that was acquired before contrast injection and two images that were acquired after contrast injection. In the proposed method, the region of interest (ROI) slice images are first extracted from each phase image. The slice images at the same position in each ROI are stacked to generate a three-dimensional (3D) tensor. A cross-phase convolution generates feature maps with the 3D tensor to incorporate the temporal information. Subsequently, the feature maps are used as the input layers for ResUNet++. New feature maps are extracted from the input data using the ResUNet++ encoders, following which the nonmass regions are segmented by a decoder. A convolutional long short-term memory layer is introduced into the decoder to analyze a sequence of slice images. When using the proposed method, the average detection accuracy of nonmasses, number of false positives, Jaccard coefficient, Dice similarity coefficient, positive predictive value, and sensitivity were 90.5%, 1.91, 0.563, 0.712, 0.714, and 0.727, respectively, larger than those obtained using 3D U-Net, V-Net, and nnFormer. The proposed method achieves high detection and shape accuracies and will be useful in differential diagnoses of nonmasses.

AUTOMATED IMAGE ALIGNMENT AND SEGMENTATION TO FOLLOW PROGRESSION OF GEOGRAPHIC ATROPHY IN AGE-RELATED MACULAR DEGENERATION

To develop a computer-based image segmentation method for standardizing the quantification of geographic atrophy (GA). The authors present an automated image segmentation method based on the fuzzy c-means clustering algorithm for the detection of GA lesions. The method is evaluated by comparing computerized segmentation against outlines of GA drawn by an expert grader for a longitudinal series of fundus autofluorescence images with paired 30° color fundus photographs for 10 patients. The automated segmentation method showed excellent agreement with an expert grader for fundus autofluorescence images, achieving a performance level of 94 ± 5% sensitivity and 98 ± 2% specificity on a per-pixel basis for the detection of GA area, but performed less well on color fundus photographs with a sensitivity of 47 ± 26% and specificity of 98 ± 2%. The segmentation algorithm identified 75 ± 16% of the GA border correctly in fundus autofluorescence images compared with just 42 ± 25% for color fundus photographs. The results of this study demonstrate a promising computerized segmentation method that may enhance the reproducibility of GA measurement and provide an objective strategy to assist an expert in the grading of images.

Computerized Assessment of Intraretinal and Subretinal Fluid Regions in Spectral-Domain Optical Coherence Tomography Images of the Retina

To evaluate a new computerized segmentation technique for the quantification of intraretinal and subretinal fluid in spectral-domain optical coherence tomography (SD OCT) images of the retina. Prospective, cross-sectional study. Thirty-seven B-scan images of 37 patients with exudative age-related macular degeneration were chosen randomly from SD OCT volume scans (1 per volume scan). All hyporeflective areas in the image first were segmented automatically as candidate regions by the program. Researchers who were masked to the candidate region information selected each fluid region from the original image using a single mouse click. The program then delineated the boundary of each region selected and calculated quantitative parameters, including total area of fluid regions if multiple regions were selected. The performance of our technique was validated by comparing the results with the measurements obtained from boundaries manually delineated by 2 masked observers. Time efficiency, agreement with manual delineation, and intraobserver and interobserver agreement of using the program were evaluated. The proposed technique reduced the average processing time per image approximately 6-fold (15seconds for computerized segmentation vs 90 seconds for manual delineation). There was good agreement between computerized segmentation and manual delineation measured by intraclass correlation coefficient (range, 0.897 to 0.979) and the Dice coefficient (range, 0.721 to 0.785). The proposed technique has excellent intraobserver and interobserver agreement (intraclass correlation coefficient range, 0.998 to 0.999; Dice coefficient range. 0.959 to 0.981). This computerized segmentation method allows for accurate and fast quantification of fluidinretinal SD OCT images and could assist in monitoring disease progression and evaluating therapeutic intervention.

Computerized Segmentation Method for Individual Calcifications Within Clustered Microcalcifications While Maintaining Their Shapes on Magnification Mammograms

In a computer-aided diagnosis (CADx) scheme for evaluating the likelihood of malignancy of clustered microcalcifications on mammograms, it is necessary to segment individual calcifications correctly. The purpose of this study was to develop a computerized segmentation method for individual calcifications with various sizes while maintaining their shapes in the CADx schemes. Our database consisted of 96 magnification mammograms with 96 clustered microcalcifications. In our proposed method, a mammogram image was decomposed into horizontal subimages, vertical subimages, and diagonal subimages for a second difference at scales 1 to 4 by using a filter bank. The enhanced subimages for nodular components (NCs) and the enhanced subimages for both nodular and linear components (NLCs) were obtained from analysis of a Hessian matrix composed of the pixel values in those subimages for the second difference at each scale. At each pixel, eight objective features were given by pixel values in the subimages for NCs at scales 1 to 4 and the subimages for NLCs at scales 1 to 4. An artificial neural network with the eight objective features was employed to enhance calcifications on magnification mammograms. Calcifications were finally segmented by applying a gray-level thresholding technique to the enhanced image for calcifications. With the proposed method, a sensitivity of calcifications within clustered microcalcifications and the number of false positives per image were 96.5% (603/625) and 1.69, respectively. The average shape accuracy for segmented calcifications was also 91.4%. The proposed method with high sensitivity of calcifications while maintaining their shapes would be useful in the CADx schemes.

Robust segmentation of mass‐lesions in contrast‐enhanced dynamic breast MR images

To develop and evaluate a computerized segmentation method for breast MRI (BMRI) mass-lesions. A computerized segmentation algorithm was developed to segment mass-like-lesions on breast MRI. The segmentation algorithm involved: (i) interactive lesion selection, (ii) automatic intensity threshold estimation, (iii) connected component analysis, and (iv) a postprocessing procedure for hole-filling and leakage removal. Seven observers manually traced the borders of all slices of 30 mass-lesions using the same tools. To initiate the computerized segmentation, each user selected a seed-point for each lesion interactively using two methods: direct seed-point and robust region of interest (ROI) selections. The manual and computerized segmentations were compared pair-wise using the measured size and overlap to evaluate similarity, and the reproducibility of the computerized segmentation was compared with the interobserver variability of the manual delineations. The observed inter- and intraobserver variations were similar (P > 0.05). Computerized segmentation using the robust ROI selection method was significantly (P < 0.001) more reproducible in measuring lesion size (stDev 1.8%) than either manual contouring (11.7%) or computerized segmentation using directly placed seed-point method (13.7%). The computerized segmentation method using robust ROI selection is more reproducible than manual delineation in terms of measuring the size of a mass-lesion.

Computerized lesion segmentation of breast ultrasound based on marker‐controlled watershed transformation

This paper presents a computerized segmentation method for breast lesions on ultrasound (US) images. It consists of first applying a contrast-enhanced approach, i.e., a contrast-limited adaptive histogram equalization. Then, aiming at removing speckle and enhancing the lesion boundary, an anisotropic diffusion filter, guided by texture descriptors derived from a set of Gabor filters, is applied. To eliminate the distant pixels that do not belong to the tumor, the resulting filtered image is multiplied by a constraint Gaussian function. By doing so, both the segmentation and the marker functions are generated and could be used in the marker-controlled watershed transformation algorithm to create potential lesion boundaries. Finally, to determine the lesion contour, the average radial derivative function is evaluated. The proposed method was tested with 50 breast US images and 60 simulated "ultrasound-like" images. Accuracy and precision of the segmentation method were then assessed. For the accuracy, three parameters were used: Overlap ratio (OR), normalized residual value (nrv), and proportional distance (PD) between contours. The average results for US images were OR = 0.86 +/- 0.05, nrv = 0.16 +/- 0.06, and PD = 6.58 +/- 2.52%. For simulated ultrasound-like images, a better performance (OR = 0.92 +/- 0.01, nrv = 0.08 +/- 0.01, and PD = 3.20 +/- 0.53%) was achieved. The segmentation method proposed was capable of delineating the lesion contours with high accuracy in comparison to both the radiologists' delineations and the true delineations of simulated images. Moreover, this method was also found to be robust to human-dependent parameters variations.

A new high‐resolution computed tomography (CT) segmentation method for trabecular bone architectural analysis

In the last decade, high-resolution computed tomography (CT) and microcomputed tomography (micro-CT) have been increasingly used in anthropological studies and as a complement to traditional histological techniques. This is due in large part to the ability of CT techniques to nondestructively extract three-dimensional representations of bone structures. Despite prior studies employing CT techniques, no completely reliable method of bone segmentation has been established. Accurate preprocessing of digital data is crucial for measurement accuracy, especially when subtle structures such as trabecular bone are investigated. The research presented here is a new, reproducible, accurate, and fully automated computerized segmentation method for high-resolution CT datasets of fossil and recent cancellous bone: the Ray Casting Algorithm (RCA). We compare this technique with commonly used methods of image thresholding (i.e., the half-maximum height protocol and the automatic, adaptive iterative thresholding procedure). While the quality of the input images is crucial for conventional image segmentation, the RCA method is robust regarding the signal to noise ratio, beam hardening, ring artifacts, and blurriness. Tests with data of extant and fossil material demonstrate the superior quality of RCA compared with conventional thresholding procedures, and emphasize the need for careful consideration of optimal CT scanning parameters.

A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images 1

Accurate quantification of the shape and extent of breast tumors has a vital role in nearly all applications of breast magnetic resonance (MR) imaging (MRI). Specifically, tumor segmentation is a key component in the computerized assessment of likelihood of malignancy. However, manual delineation of lesions in four-dimensional MR images is labor intensive and subject to interobserver and intraobserver variations. We developed a computerized lesion segmentation method that has the advantage of being automatic, efficient, and objective. We present a fuzzy c-means (FCM) clustering-based method for the segmentation of breast lesions in three dimensions from contrast-enhanced MR images. The proposed lesion segmentation algorithm consists of six consecutive stages: region of interest (ROI) selection by a human operator, lesion enhancement within the selected ROI, application of FCM on the enhanced ROI, binarization of the lesion membership map, connected-component labeling and object selection, and hole-filling on the selected object. We applied the algorithm to a clinical MR database consisting of 121 primary mass lesions. Manual segmentation of the lesions by an expert MR radiologist served as a reference in the evaluation of the computerized segmentation method. We also compared the proposed algorithm with a previously developed volume-growing (VG) method. For the 121 mass lesions in our database, 97% of lesions were segmented correctly by means of the proposed FCM-based method at an overlap threshold of 0.4, whereas 84% of lesions were correctly segmented by means of the VG method. Our proposed algorithm for breast-lesion segmentation in dynamic contrast-enhanced MRI was shown to be effective and efficient.