Biomedical Computer Vision Group, BIOQUANT, IPMB, Heidelberg University and DKFZ, Im Neuenheimer Feld 267, Heidelberg 69120, Germany.
Image and Pattern Analysis Group, Heidelberg University, Heidelberg 69120, Germany.
Med Image Anal. 2019 Dec;58:101536. doi: 10.1016/j.media.2019.101536. Epub 2019 Jul 19.
Accurate and efficient segmentation of cell nuclei in fluorescence microscopy images plays a key role in many biological studies. Besides coping with image noise and other imaging artifacts, the separation of touching and partially overlapping cell nuclei is a major challenge. To address this, we introduce a globally optimal model-based approach for cell nuclei segmentation which jointly exploits shape and intensity information. Our approach is based on implicitly parameterized shape models, and we propose single-object and multi-object schemes. In the single-object case, the used shape parameterization leads to convex energies which can be directly minimized without requiring approximation. The multi-object scheme is based on multiple collaborating shapes and has the advantage that prior detection of individual cell nuclei is not needed. This scheme performs joint segmentation and cluster splitting. We describe an energy minimization scheme which converges close to global optima and exploits convex optimization such that our approach does not depend on the initialization nor suffers from local energy minima. The proposed approach is robust and computationally efficient. In contrast, previous shape-based approaches for cell segmentation either are computationally expensive, not globally optimal, or do not jointly exploit shape and intensity information. We successfully applied our approach to fluorescence microscopy images of five different cell types and performed a quantitative comparison with previous methods.
在荧光显微镜图像中准确而高效地分割细胞核在许多生物学研究中起着关键作用。除了应对图像噪声和其他成像伪影外，分离接触和部分重叠的细胞核也是一个主要挑战。针对这个问题，我们引入了一种基于全局最优模型的方法来进行细胞核分割，该方法联合利用了形状和强度信息。我们的方法基于隐式参数化的形状模型，并提出了单目标和多目标方案。在单目标情况下，所使用的形状参数化导致凸能量，可以直接最小化，而不需要近似。多目标方案基于多个协作形状，具有不需要单独检测每个细胞核的优点。该方案执行联合分割和聚类分裂。我们描述了一种能量最小化方案，该方案能够收敛到接近全局最优的解，并利用凸优化，因此我们的方法不依赖于初始化，也不会受到局部能量最小值的影响。所提出的方法具有鲁棒性和高效的计算能力。相比之下，之前基于形状的细胞分割方法要么计算成本高，要么不是全局最优的，要么没有联合利用形状和强度信息。我们成功地将我们的方法应用于五种不同细胞类型的荧光显微镜图像，并与以前的方法进行了定量比较。
