You-Hsuan Liu

Segmenting cells in 3D fluorescence microscopy has meant choosing between two paths: deep learning is efficient but returns discrete voxel masks, while active surfaces give continuous shape but struggle with multiple objects. Nagini3D joins them, using a 3D U-Net to predict both object locations and spline surface parameters, so each object is one closed smooth parametric surface. On the synthetic CAPS set of non-star-convex objects, the refined Nagini3D+ reaches a mean IoU of 0.859 against StarDist3D's 0.779; on the real BONES set the two are close, but Nagini3D is the fastest tested, at 4.75 seconds on average.

Segmenting cells in 3D fluorescence microscopy has meant choosing between two paths: deep learning is efficient but returns discrete voxel masks, while active surfaces give continuous shape but struggle with multiple objects. Nagini3D joins them, using a 3D U-Net to predict both object locations and spline surface parameters, so each object is one closed smooth parametric surface. On the synthetic CAPS set of non-star-convex objects, the refined Nagini3D+ reaches a mean IoU of 0.859 against StarDist3D's 0.779; on the real BONES set the two are close, but Nagini3D is the fastest tested, at 4.75 seconds on average.