A deep learning model for detection and tracking in high-throughput images of organoid

Abstract

Organoid, an in vitro 3D culture, has extremely high similarity with its source organ or tissue, which creates a model in vitro that simulates the in vivo environment. Organoids have been extensively studied in cell biology, precision medicine, drug toxicity, efficacy tests, etc., which have been proven to have high research value. Periodic observation of organoids in microscopic images to obtain morphological or growth characteristics is essential for organoid research. It is difficult and time-consuming to perform manual screens for organoids, but there is no better solution in the prior art. In this paper, we established the first high-throughput organoid image dataset for organoids detection and tracking, which experienced experts annotate in detail. Moreover, we propose a novel deep neural network (DNN) that effectively detects organoids and dynamically tracks them throughout the entire culture. We divided our solution into two steps: First, the high-throughput sequential images are processed frame by frame to detect all organoids; Second, the similarities of the organoids in the adjacent frames are computed, and the organoids on the adjacent frames are matched in pairs. With the help of our proposed dataset, our model achieves organoids detection and tracking with fast speed and high accuracy, effectively reducing the burden on researchers. To our knowledge, this is the first exploration of applying deep learning to organoid tracking tasks. Experiments have demonstrated that our proposed method achieved satisfactory results on organoid detection and tracking, verifying the great potential of deep learning technology in this field.