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An adaptive algorithm to identify ambiguous prostate capsule boundary lines for three-dimensional reconstruction and quantitation.

机译:一种自适应算法,用于识别不明确的前列腺包膜边界线,以进行三维重建和定量。

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Currently there are few parameters that are used to compare the efficiency of different methods of cancerous prostate surgical removal. An accurate assessment of the percentage and depth of extra-capsular soft tissue removed with the prostate by the various surgical techniques can help surgeons determine the appropriateness of surgical approaches. Additionally, an objective assessment can allow a particular surgeon to compare individual performance against a standard. In order to facilitate 3D reconstruction and objective analysis and thus provide more accurate quantitation results when analyzing specimens, it is essential to automatically identify the capsule line that separates the prostate gland tissue from its extra-capsular tissue. However the prostate capsule is sometimes unrecognizable due to the naturally occurring intrusion of muscle and connective tissue into the prostate gland. At these regions where the capsule disappears, its contour can be arbitrarily reconstructed by drawing a continuing contour line based on the natural shape of the prostate gland. Presented here is a mathematical model that can be used in deciding the missing part of the capsule. This model approximates the missing parts of the capsule where it disappears to a standard shape by using a Generalized Hough Transform (GHT) approach to detect the prostate capsule. We also present an algorithm based on a least squares curve fitting technique that uses a prostate shape equation to merge previously detected capsule parts with the curve equation to produce an approximated curve that represents the prostate capsule. We have tested our algorithms using three shapes on 13 prostate slices that are cut at different locations from the apex and the results are promising.
机译:当前,很少有参数可用于比较不同的前列腺癌手术切除方法的效率。通过各种手术技术对前列腺切除的囊外软组织的百分比和深度进行准确评估,可以帮助外科医生确定手术方法的适当性。此外,客观评估可以使特定的外科医生将个人表现与标准进行比较。为了便于3D重建和客观分析,从而在分析标本时提供更准确的定量结果,自动识别将前列腺腺体组织与其囊外组织分开的囊膜线至关重要。但是,由于天然发生的肌肉和结缔组织侵入前列腺,有时无法识别前列腺囊。在胶囊消失的这些区域,可以通过根据前列腺的自然形状画一条连续的轮廓线来任意重建其轮廓。这里介绍的是一个数学模型,可用于确定胶囊的缺失部分。该模型通过使用通用霍夫变换(GHT)方法来检测前列腺胶囊,从而近似估计胶囊的缺失部分,在该部分消失为标准形状。我们还提出了一种基于最小二乘曲线拟合技术的算法,该算法使用前列腺形状方程式将先前检测到的胶囊部分与曲线方程式合并,以生成代表前列腺胶囊的近似曲线。我们已经在13个前列腺切片上使用三种形状测试了我们的算法,这些切片从根尖的不同位置切开,结果令人鼓舞。

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