As neural network models that accurately segment “unseen” types of data signify longevity and wider applicability, we examined the generalization capacity of the SpinX framework. Our training dataset consisted of spindles labeled using either mCherry-Tubulin or SiR-Tubulin dye, markers of tubulin subunits (Fig. 3 a) which are responsible for assembling and disassembling microtubules of the mitotic spindle (reviewed in Tamura and Draviam 2012). To examine the extent to which SpinX can be generalized, we evaluated the accuracy of SpinX in detecting spindles in time-lapse movie datasets where two different fluorescent marker proteins were fused to two distinct microtubule-binding proteins. First, we tested image datasets of cells expressing YFP-tagged Astrin, a microtubule-wall binding protein that can be found at the chromosome–microtubule attachment site soon after the tethering of microtubule ends to chromosomes (Conti et al., 2019; n = 330 images from 10 cells; Fig. 3 b). Model evaluation was carried out by an expert user through manual binary classification of either “correct” or “incorrect” prediction. Expert evaluation showed that SpinX can successfully segment spindles in movies of YFP-Astrin expressing cells with an 88% accuracy (Fig. 3 c). The images in the YFP-Astrin dataset were not complete images of the entire mitotic cell but instead cropped images encompassing the spindle structure alone, requiring padding (see Materials and methods) to allow segmentation through SpinX. Next, we tested image datasets of cells expressing mRFP-tagged End-Binding 3 (EB3), a growing microtubule-end binding protein (Komarova et al., 2005) that can be found at the chromosome–microtubule attachment site and spindle poles (Tamura and Draviam 2012; n = 1,540 images from 5 cells; Fig. 3 b). In addition to widefield images, we extended our evaluation to high-resolution confocal images of cells expressing mKate2-EB3 (Fig. 3 b; n = 1,920 images from 5 cells). Expert evaluation showed that SpinX is equally successful in segmenting spindles of EB3 marker expressing cells in both widefield and confocal microscopy images, with a 95% and 96% accuracy, respectively (Fig. 3, c and d). To determine the extent to which SpinX can successfully segment spindles in images of new cell types and embryos, and images acquired using different microscope systems, we used images generated by others and made available as Spindle3D datasets (Kletter et al., 2022). In spindles of different cell types, mESC and HEK293, spindle pole inclusion was successful 100% and 92% of the cases, respectively, despite being imaged using different imaging systems; however, this segmentation efficiency was reduced in large spindles of bovine embryos (Fig. 3, e and f). Nevertheless, in cultured cells, widefield and confocal images of spindles were equally well segmented using SpinX (Fig. 3, d and f). Thus, the successful segmentation of EB3 or Astrin protein marker labeled spindles, and Tubulin labeled spindles in different cell types and distinct microscopy systems demonstrate a striking generalization capacity of the SpinX framework for a variety of spindle markers, cell types, and microscopy methods.