Ko Sugawara

Automahout: an autonomous image analysis system for cell tracking

Automahout autonomously tracks cells in time-lapse images using a self-tuning system that annotates, trains, and refines models without human input, achieving high TRA scores on public datasets.

Mastodon: the Command Center for Large-Scale Lineage-Tracing Microscopy Datasets

Mastodon is a software platform for analyzing large-scale lineage-tracing microscopy datasets, enabling manual and automated cell tracking and lineage analysis in complex 3D geometries.

Long-term live imaging, cell identification and cell tracking in regenerating crustacean legs

The paper presents a method for long-term live imaging and cell tracking during crustacean leg regeneration, enabling detailed analysis of cell fates and lineages.

Volumetric trans-scale imaging of massive quantity of heterogeneous cell populations in centimeter-wide tissue and embryo

The paper introduces a high-resolution volumetric imaging system for observing cellular dynamics in large 3D tissues and embryos, demonstrating its application in mouse brain and quail embryo studies.

Training deep learning models for cell image segmentation with sparse annotations

The paper shows that deep learning models for cell image segmentation can be trained effectively with sparse annotations and demonstrates the use of SAM to build such models quickly.

Tracking cell lineages in 3D by incremental deep learning

ELEPHANT enables accurate 3D cell lineage tracking in crustacean leg regeneration using an incremental deep learning approach with minimal user effort.