Automatic segmentation of MRI in prospective breast volume evaluation: Comparison of different assessments for immediate breast reconstruction.

BACKGROUND: Assessment of breast volume is essential in preoperative planning of immediate breast reconstruction (IBR) surgery to achieve satisfactory cosmetic outcome. This study introduced a breast volume measurement tool that can be used to perform automatic segmentation of magnetic resonance images (MRI) and calculation of breast volume. We compared the accuracy and reliability of this measurement method with four other conventional modalities. METHODS: Patients who were scheduled to undergo mastectomy with IBR between 2016 and 2021 were enrolled in the study. Five different breast volume assessments, including automatic segmentation of MRI, manual segmentation of MRI, 3D surface imaging, mammography, and the BREAST-V formula, were used to evaluate different breast volumes. The results were validated using water displacement volumes of the mastectomy specimens. RESULTS: In this pilot study, a total of 50 female patients met the inclusion criteria and contributed 54 breast specimens to the volumetric analysis. There was a strong linear association between the MRI and water displacement methods (automatic segmentation: r = 0.911, p < 0.001; manual segmentation: r = 0.924, p < 0.001), followed by 3D surface imaging (r = 0.858, p < 0.001), mammography (r = 0.841, p < 0.001), and Breast-V formula (r = 0.838, p < 0.001). Breast volumes measured using automatic and manual segmentation of MRI had lower mean relative errors (30.3% ± 22.0% and 28.9% ± 19.8, respectively) than 3D surface imaging (38.9% ± 31.2), Breast-V formula (44.8% ± 25.8), and mammography (60.3% ± 37.6). CONCLUSION: Breast volume assessment using the MRI methods had better accuracy and reliability than the other methods used in our study. Breast volume measurement using automatic segmentation of MRI could be more efficient compared to the conventional methods.

Modeling Bidirectional Texture Functions with Multivariate Spherical Radial Basis Functions.

This paper presents a novel parametric representation for bidirectional texture functions. Our method mainly relies on two original techniques, namely, multivariate spherical radial basis functions (SRBFs) and optimized parameterization. First, since the surface appearance of a real-world object is frequently a mixed effect of different physical factors, the proposed sum-of-products model based on multivariate SRBFs especially provides an intrinsic and efficient representation for heterogenous materials. Second, optimized parameterization particularly aims at overcoming the major disadvantage of traditional fixed parameterization. By using a parametric model to account for variable transformations, the parameterization process can be tightly integrated with multivariate SRBFs into a unified framework. Finally, a hierarchical fitting algorithm for bidirectional texture functions is developed to exploit spatial coherence and reduce computational cost. Our experimental results further reveal that the proposed representation can easily achieve high-quality approximation and real-time rendering performance.

Background removal of multiview images by learning shape priors.

Image-based rendering has been successfully used to display 3-D objects for many applications. A well-known example is the object movie, which is an image-based 3-D object composed of a collection of 2-D images taken from many different viewpoints of a 3-D object. In order to integrate image-based 3-D objects into a chosen scene (e.g., a panorama), one has to meet a hard challenge--to efficiently and effectively remove the background from the foreground object. This problem is referred to as multiview images (MVIs) segmentation. Another task requires MVI segmentation is image-based 3-D reconstruction using multiview images. In this paper, we propose a new method for segmenting MVI, which integrates some useful algorithms, including the well-known graph-cut image segmentation and volumetric graph-cut. The main idea is to incorporate the shape prior into the image segmentation process. The shape prior introduced into every image of the MVI is extracted from the 3-D model reconstructed by using the volumetric graph cuts algorithm. Here, the constraint obtained from the discrete medial axis is adopted to improve the reconstruction algorithm. The proposed MVI segmentation process requires only a small amount of user intervention, which is to select a subset of acceptable segmentations of the MVI after the initial segmentation process. According to our experiments, the proposed method can provide not only good MVI segmentation, but also provide acceptable 3-D reconstructed models for certain less-demanding applications.

Comparison between immersion-based and toboggan-based watershed image segmentation.

Watershed segmentation has recently become a popular tool for image segmentation. There are two approaches to implementing watershed segmentation: immersion approach and toboggan simulation. Conceptually, the immersion approach can be viewed as an approach that starts from low altitude to high altitude and the toboggan approach as an approach that starts from high altitude to low altitude. The former seemed to be more popular recently (e.g., Vincent and Soille), but the latter had its own supporters (e.g., Mortensen and Barrett). It was not clear whether the two approaches could lead to exactly the same segmentation result and which approach was more efficient. In this paper, we present two "order-invariant" algorithms for watershed segmentation, one based on the immersion approach and the other on the toboggan approach. By introducing a special RIDGE label to achieve the property of order-invariance, we find that the two conceptually opposite approaches can indeed obtain the same segmentation result. When running on a Pentium-III PC, both of our algorithms require only less than 1/30 s for a 256 x 256 image and 1/5 s for a 512 x 512 image, on average. What is more surprising is that the toboggan algorithm, which is less well known in the computer vision community, turns out to run faster than the immersion algorithm for almost all the test images we have used, especially when the image is large, say, 512 x 512 or larger. This paper also gives some explanation as to why the toboggan algorithm can be more efficient in most cases.