Multi-Scale and Shape Constrained Localized Region-Based Active Contour Segmentation of Uterine Fibroid Ultrasound Images in HIFU Therapy

Abstract

PurposeTo overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.MethodsWe incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.ResultsExperimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).ConclusionsAn accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.

Modeling and Predicting Tissue Movement and Deformation for High Intensity Focused Ultrasound Therapy

“…We tested the proposed GLCV model on HIFU ultrasound images of the target tissue and compared our GLCV based method with other well-known methods in segmenting HIFU ultrasound images of the target tissue, including GAC [ 19 ], CV [ 20 ], LCV [ 22 ], RSF [ 23 ] and MSLCV [ 24 ]. Fig 2 presents the experimental results.…”

“…For images A and B, the initial contour is an ellipse, and for images C, D, E and F, the initial contours are defined by 5–7 connecting points. Columns 2 to 7 show, respectively, the segmentation results for GAC [ 19 ], CV [ 20 ], LCV [ 22 ], RSF [ 23 ], MSLCV [ 24 ] and GLCV. The green curves are manual segmentation results by an experienced doctor as the ground truth, and the red curves are the final segmentation contours from these methods.…”

“…Recently, Liao et al . [ 24 ] presented a multi-scale and shape constrained localized CV model (MSLCV) to segment uterine fibroid ultrasound images in HIFU therapy and achieved good segmentation results. It adopts a multi-scale segmentation method to improve segmentation efficiency.…”

A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM) in ultrasound images

“…However, shape and local texture measures, such as the Gabor texture [ 5 ], are commonly used to increase the accuracy of the segmentation process. Several research groups have introduced shape-based methods, such as the active contour model (ACM) and active shape model (ASM), for tissue segmentation in ultrasound images [ 6 – 15 ]. Hamameh and Gustavsson [ 6 ] presented a method that combined the ASM with an ACM to clarify the boundary of the left ventricle in echocardiograms.…”

Adaptive localised region and edge‐based active contour model using shape constraint and sub‐global information for uterine fibroid segmentation in ultrasound‐guided HIFU therapy

“…Tran et al [30] presented a fuzzy energy‐based active contour model with shape prior for image segmentation, where the shape term is defined as the distance between the evolving shape and a reference one, constrains the evolving contour with respect to the reference shape. A multi‐scale shape constrained LCV (MSLCV) model was proposed to obtain good segmentation in ultrasound images and it can reduce boundary leakage or excessive contraction by the shape constraint [31]. However, the performance of these models in ultrasound images is sensitive to the wake boundary as well as the initialisation of the constrained shape, and the segmentation is still inefficient for the uterine fibroid segmentation in ultrasound‐guided HIFU therapy.…”