Deep neural networks have been tremendously successful at segmenting objects in images. However, it has been shown they still have limitations on challenging problems such as the segmentation of medical images. The main reason behind this lower success resides in the reduced size of the object in the image. In this paper we overcome this limitation through a cyclic collaborative framework, CyCoSeg. The proposed framework is based on a deep active shape model (D-ASM), which provides prior information about the shape of the object, and a semantic segmentation network (SSN). These two models collaborate to reach the desired segmentation by influencing each other: SSN helps D-ASM identify relevant keypoints in the image through an Expectation Maximization formulation, while D-ASM provides a segmentation proposal that guides the SSN. This cycle is repeated until both models converge. Extensive experimental evaluation shows CyCoSeg boosts the performance of the baseline models, including several popular SSNs, while avoiding major architectural modifications. The effectiveness of our method is demonstrated on the left ventricle segmentation on two benchmark datasets, where our approach achieves one of the most competitive results in segmentation accuracy. Furthermore, its generalization is demonstrated for lungs and kidneys segmentation in CT scans.

中文翻译：

---

CyCoSeg：用于自动医学图像分割的循环协作框架。

深度神经网络在分割图像中的对象方面取得了巨大成功。然而，已经表明它们在诸如医学图像分割等具有挑战性的问题上仍然存在局限性。成功率较低的主要原因在于图像中物体尺寸的减小。在本文中，我们通过循环协作框架 CyCoSeg 克服了这一限制。所提出的框架基于深度主动形状模型 (D-ASM)，它提供有关对象形状的先验信息，以及语义分割网络 (SSN)。这两个模型通过相互影响协作以达到所需的分割：SSN 通过期望最大化公式帮助 D-ASM 识别图像中的相关关键点，而 D-ASM 提供指导 SSN 的分割建议。重复这个循环，直到两个模型收敛。广泛的实验评估表明 CyCoSeg 提高了基线模型的性能，包括几个流行的 SSN，同时避免了重大的架构修改。我们的方法的有效性在两个基准数据集的左心室分割上得到了证明，我们的方法在分割精度方面取得了最具竞争力的结果之一。此外，它的泛化在 CT 扫描中的肺部和肾脏分割中得到证明。我们的方法在分割准确性方面取得了最具竞争力的结果之一。此外，它的泛化在 CT 扫描中的肺部和肾脏分割中得到证明。我们的方法在分割准确性方面取得了最具竞争力的结果之一。此外，它的泛化在 CT 扫描中的肺部和肾脏分割中得到证明。