Dataset for Automated Estimation of Drain Output in Postoperative Patients Using Deep Learning on Clinical Images

This is the accompanying dataset to our paper titled "Automated Estimation of Drain Output in Postoperative Patients Using Deep Learning on Clinical Images". In this study, we present a deep learning based automated system for estimating postoperative drain output from clinical images of Jackson-Pratt drains and drainage bags collected under real hospital conditions. The dataset includes a wide range of effluents, such as blood, gastric content, and serous fluid, representing the visual variability encountered in daily clinical practice. The proposed pipeline combines object detection and semantic segmentation to localize drains and mark out fluid boundaries, enabling precise and contact-free volume estimation of the fluids inside the drainage bags. 

The dataset includes both the images, the labels, and the semantic segmentations.  The actual volumes are provided in the names of each image. The semantic segmentations are labeled with values 1, 2, 3. This is only part of the dataset used in the work. Please email us with a reasonable request if you need to access more data.

Please cite our work if you use the data: 

M. E. Yuksel, A. Celikkaya, S. E. Erdem, M. Akinci, "Automated Estimation of Drain Output in Postoperative Patients Using Deep Learning on Clinical Images," Scientific Reports, 2025.