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Computer-aided detection of bladder tumors based on the thickness mapping of bladder wall in MR images

机译：基于MR图像膀胱壁厚度映射的计算机辅助检测膀胱肿瘤

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Bladder cancer is reported to be the fifth leading cause of cancer deaths in the United States. Recent advances in medical imaging technologies, such as magnetic resonance (MR) imaging, make virtual cystoscopy a potential alternative with advantages as being a safe and non-invasive method for evaluation of the entire bladder and detection of abnormalities. To help reducing the interpretation time and reading fatigue of the readers or radiologists, we introduce a computer-aided detection scheme based on the thickness mapping of the bladder wall since locally-thickened bladder wall often appears around tumors. In the thickness mapping method, the path used to measure the thickness can be determined without any ambiguity by tracing the gradient direction of the potential field between the inner and outer borders of the bladder wall. The thickness mapping of the three-dimensional inner border surface of the bladder is then flattened to a two-dimensional (2D) gray image with conformal mapping method. In the 2D flattened image, a blob detector is applied to detect the abnormalities, which are actually the thickened bladder wall indicating bladder lesions. Such scheme was tested on two MR datasets, one from a healthy volunteer and the other from a patient with a tumor. The result is preliminary, but very promising with 100% detection sensitivity at 7 FPs per case.
机译：据报道，膀胱癌是美国癌症死亡的第五个主要原因。医学成像技术的最新进展，例如磁共振（MR）成像，使虚拟膀胱镜检查具有优势的潜在替代方案，作为一种安全和非侵入性的方法，用于评估整个膀胱和异常的检测。为了帮助减少读者或放射科学家的解释时间和读取疲劳，我们引入基于膀胱壁的厚度映射的计算机辅助检测方案，因为局部加厚的膀胱壁通常出现在肿瘤周围。在厚度映射方法中，可以通过追踪膀胱壁的内边界和外边界之间的电位场的梯度方向而没有任何歧义，可以确定用于测量厚度的路径。然后将囊的三维内边界表面的厚度映射到具有共形映射方法的二维（2D）灰度图像。在2D展平图像中，施加BLOB检测器以检测异常，实际上是表示膀胱病变的增稠膀胱壁。这些方案在两个先生数据集上进行了测试，其中一个来自健康志愿者，另一个来自患者的患者。结果是初步的，但在每种情况下具有100％的检测灵敏度为100％的检测灵敏度。

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