Mitochondria display complex morphology and movements, which complicates their segmentation and tracking in time-lapse images. Here, we introduce Mitometer, an algorithm for fast, unbiased, and automated segmentation and tracking of mitochondria in live-cell two-dimensional and three-dimensional time-lapse images. Mitometer requires only the pixel size and the time between frames to identify mitochondrial motion and morphology, including fusion and fission events. The segmentation algorithm isolates individual mitochondria via a shape- and size-preserving background removal process. The tracking algorithm links mitochondria via differences in morphological features and displacement, followed by a gap-closing scheme. Using Mitometer, we show that mitochondria of triple-negative breast cancer cells are faster, more directional, and more elongated than those in their receptor-positive counterparts. Furthermore, we show that mitochondrial motility and morphology in breast cancer, but not in normal breast epithelia, correlate with metabolic activity. Mitometer is an unbiased and user-friendly tool that will help resolve fundamental questions regarding mitochondrial form and function.

线粒体显示出复杂的形态和运动，这使得它们在延时图像中的分割和跟踪变得复杂。在这里，我们介绍了 Mitometer，这是一种用于在活细胞二维和三维延时图像中快速、无偏和自动分割和跟踪线粒体的算法。Mitometer 只需要像素大小和帧之间的时间来识别线粒体运动和形态，包括融合和裂变事件。分割算法通过保持形状和大小的背景去除过程分离单个线粒体。跟踪算法通过形态特征和位移的差异连接线粒体，然后是间隙闭合方案。使用 Mitometer，我们表明三阴性乳腺癌细胞的线粒体更快、更有方向性、并且比它们的受体阳性对应物更细长。此外，我们表明乳腺癌中的线粒体运动和形态与代谢活动相关，但在正常乳腺上皮细胞中却没有。Mitometer 是一种公正且用户友好的工具，将有助于解决有关线粒体形式和功能的基本问题。