Static Segmentation by Tracking: A Label-Efficient Approach for Fine-Grained Specimen Image Segmentation

Zhenyang Feng; Zihe Wang; Jianyang Gu; Saul Ibaven Bueno; Tomasz Frelek; Advikaa Ramesh; Jingyan Bai; Lemeng Wang; Zanming Huang; Jinsu Yoo; Tai-Yu Pan; Arpita Chowdhury; Michelle Ramirez; Elizabeth G. Campolongo; Matthew J. Thompson; Christopher G. Lawrence; Sydne Record; Neil Rosser; Anuj Karpatne; Daniel Rubenstein; Hilmar Lapp; Charles V. Stewart; Tanya Berger-Wolf; Yu Su; Wei-Lun Chao

arXiv · 2501.06749

Static Segmentation by Tracking: A Label-Efficient Approach for Fine-Grained Specimen Image Segmentation

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Abstract

We study image segmentation in the biological domain, particularly trait segmentation from specimen images (e.g., butterfly wing stripes, beetle elytra). This fine-grained task is crucial for understanding the biology of organisms, but it traditionally requires manually annotating segmentation masks for hundreds of images per species, making it highly labor-intensive. To address this challenge, we propose a label-efficient approach, Static Segmentation by Tracking (SST), based on a key insight: while specimens of the same species exhibit natural variation, the traits of interest show up consistently. This motivates us to concatenate specimen images into a ``pseudo-video'' and reframe trait segmentation as a tracking problem. Specifically, SST generates masks for unlabeled images by propagating annotated or predicted masks from the ``pseudo-preceding'' images. Built upon recent video segmentation models, such as Segment Anything Model 2, SST achieves high-quality trait segmentation with only one labeled image per species, marking a breakthrough in specimen image analysis. To further enhance segmentation quality, we introduce a cycle-consistent loss for fine-tuning, again requiring only one labeled image. Additionally, we demonstrate the broader potential of SST, including one-shot instance segmentation in natural images and trait-based image retrieval.

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Zhenyang Feng, Zihe Wang, Jianyang Gu, Saul Ibaven Bueno, Tomasz Frelek, Advikaa Ramesh, Jingyan Bai, Lemeng Wang, Zanming Huang, Jinsu Yoo, Tai-Yu Pan, Arpita Chowdhury, Michelle Ramirez, Elizabeth G. Campolongo, Matthew J. Thompson, Christopher G. Lawrence, Sydne Record, Neil Rosser, Anuj Karpatne, Daniel Rubenstein, Hilmar Lapp, Charles V. Stewart, Tanya Berger-Wolf, Yu Su, Wei-Lun Chao. 2025-01-12. Static Segmentation by Tracking: A Label-Efficient Approach for Fine-Grained Specimen Image Segmentation. https://arxiv.org/abs/2501.06749

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