3D Mitochondria Instance Segmentation with Spatio-Temporal Transformers
This addresses the challenge of accurately segmenting mitochondria instances for biological analysis, representing an incremental improvement over existing methods.
The paper tackles the problem of 3D mitochondria instance segmentation in electron microscopy data by proposing a hybrid encoder-decoder framework with split spatio-temporal attention and an adversarial loss, achieving state-of-the-art results on three benchmarks.
Accurate 3D mitochondria instance segmentation in electron microscopy (EM) is a challenging problem and serves as a prerequisite to empirically analyze their distributions and morphology. Most existing approaches employ 3D convolutions to obtain representative features. However, these convolution-based approaches struggle to effectively capture long-range dependencies in the volume mitochondria data, due to their limited local receptive field. To address this, we propose a hybrid encoder-decoder framework based on a split spatio-temporal attention module that efficiently computes spatial and temporal self-attentions in parallel, which are later fused through a deformable convolution. Further, we introduce a semantic foreground-background adversarial loss during training that aids in delineating the region of mitochondria instances from the background clutter. Our extensive experiments on three benchmarks, Lucchi, MitoEM-R and MitoEM-H, reveal the benefits of the proposed contributions achieving state-of-the-art results on all three datasets. Our code and models are available at https://github.com/OmkarThawakar/STT-UNET.