CZII CryoET Object Identification Challenge - 2nd place solution - LuoZiqian&Lion

Deposition Overview

The 2nd place solution employs an ensemble of multiple lightweight segmentation models with parameter sizes ranging from 873K to 14.2M, including architectures such as UNet3D, VoxResNet, VoxHRNet, SegResNet, DenseVNet, and UNet2E3D. The models are trained using Tversky Loss, Dice Loss, and Cross-Entropy Loss with customized mask radii for each particle type, utilizing InstanceNorm3d and PReLU for enhanced training stability. After segmentation, particle centroids are computed using CC3D and filtered based on voxel count statistics, with an ensemble strategy involving averaging of 7 to 10 complementary models and test-time augmentation.

Annotation Methods Summary