Robust 4D reconstruction of deformable endoscopic scenes from monocular videos remains challenging due to non-rigid tissue deformation, illumination changes, occlusions, and unknown camera trajectories. Reliable dynamic reconstruction of endoscopies could significantly enhance visualization, diagnostic accuracy and intraoperative guidance of minimally invasive procedures. We propose SurgicalTS, a self-supervised triangle-splatting framework for dynamic endoscopic scene reconstruction from static monocular cameras. The scene is represented by a canonical set of triangles jointly optimized with a deformation network. We introduce a quaternion-based deformation model, an improved color parametrization, spatio-temporal priors, and a redefined initialization strategy that improves completeness under occlusions. Extensive evaluation on challenging in-vivo endoscopy videos from diverse procedures shows that SurgicalTS successfully models soft-tissue deformations and tool-tissue interactions over time, while consistently outperforming state-of-the-art methods. Our method also reduces training times while maintaining competitive geometric and photometric accuracy results, highlighting the effectiveness of quaternion-based deformation modelling with physical consistency for robust and efficient 4D reconstruction of endoscopic scenes.

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