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首页> 外文会议>International conference on advanced concepts for intelligent vision systems >Prostate Size Inference from Abdominal Ultrasound Images with Patch Based Prior Information
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Prostate Size Inference from Abdominal Ultrasound Images with Patch Based Prior Information

机译：从腹部超声图像与基于补丁的先验信息推断前列腺大小

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Prostate size inference from abdominal ultrasound images is crucial for many medical applications but it remains a challenging task due to very weak prostate borders and high image noise. This paper presents a novel method that enforces image patch prior information on multi-task deep learning followed by a global prostate shape estimation. The patch prior information is learned by multi-task Deep Convolutional Neural Networks (DCNNs) trained on multi-scale image patches to capture both local and global image information. We produce tens of thousands of image patches for the DCNN training that needs a large amount of training data which usually is not available for medical images. The three learned tasks for the DCNN are the distance between the patch center and the nearest contour point, the angle of the line segment between the patch center and the prostate center, and the contour curvature value for the patch center. During the prostate shape inference time, the labels returned from the multi-task DCNN are used in a global shape fitting process to obtain the final prostate contours which are then used for size inference. We performed experiments on transverse abdominal ultrasound images which are very challenging for automatic analysis.
机译：从腹部超声图像推断前列腺大小对于许多医学应用而言至关重要，但由于前列腺边界很弱且图像噪声较高，因此仍然是一项艰巨的任务。本文提出了一种在多任务深度学习上强制执行图像补丁先验信息，然后进行全局前列腺形状估计的新颖方法。补丁先验信息是通过在多尺度图像补丁上训练的多任务深度卷积神经网络（DCNN）学习的，以捕获本地和全局图像信息。我们为DCNN训练生成了成千上万的图像补丁，需要大量的训练数据，而这些数据通常不适用于医学图像。 DCNN的三个学习任务是贴片中心和最近的轮廓点之间的距离，贴片中心和前列腺中心之间线段的角度以及贴片中心的轮廓曲率值。在前列腺形状推断期间，将从多任务DCNN返回的标签用于全局形状拟合过程中，以获取最终的前列腺轮廓，然后将其用于尺寸推断。我们在腹部横向超声图像上进行了实验，这对自动分析非常具有挑战性。

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