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A Learning-Guided Hierarchical Approach for Biomedical Image Segmentation

机译：生物医学图像分割的学习引导分层方法

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摘要

In recent years, deep convolutional networks have been widely used for a variety of visual recognition tasks, including biomedical applications. In most studies related to biomedical domain (e.g., cell tracking), the first step is to perform symmetric segmentation on target images. Such image datasets have usually the following challenges: (1) they lack human labeled training data, (2) the locations of the objects in images are as equally important as classifying them, and (3) the result accuracy is more critical than that in traditional image segmentation. To address these problems, recent studies employ large deep neural networks to perform segmentation on biomedical images. However, such neural network approaches are very compute intensive due to the high resolution and large quantity of electron microscopy data. Additionally, some of the efforts that make use of neural network models involve redundancy as target biomedical images usually contain smaller regions of interest. Motivated by these observations, in this paper, we propose and experimentally evaluate a more efficient framework, especially suited for image segmentation on embedded systems. This approach involves first “tiling” the target image, followed by processing the tiles that only contain an object of interest in a hierarchical fashion. Our detailed experimental evaluations using four different datasets indicate that our tiling-based approach can save about 61% of execution time on average, while achieving, at the same time, a slightly higher accuracy compared to the baseline (state of the art) approach.
机译：近年来，深度卷积网络已广泛用于各种可视识别任务，包括生物医学应用。在与生物医学域（例如，小区跟踪）相关的大多数研究中，第一步是在目标图像上执行对称分割。这样的图像数据集通常是以下挑战：（1）它们缺少人类标记的训练数据，（2）图像中对象的位置与分类它们同样重要，并且（3）结果精度比其更重要传统图像分割。为了解决这些问题，最近的研究采用了大型深度神经网络来对生物医学图像进行分割。然而，由于高分辨率和大量的电子显微镜数据，这种神经网络方法非常计算密集型。另外，利用神经网络模型的一些努力涉及冗余，因为目标生物医学图像通常包含较小的感兴趣区域。在本文中，通过这些观察结果，我们提出并通过实验评估了更有效的框架，特别适用于嵌入式系统的图像分割。该方法涉及首先“平铺”目标图像，然后处理仅包含分层时尚感兴趣对象的瓦片。我们使用四个不同数据集的详细实验评估表明我们的TILES系列方法平均可以节省约61％的执行时间，同时与基线（现有技术）方法相比略高的准确度。

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