A deep learning model for drug screening and evaluation in bladder cancer organoids

Shudi Zhang; Lu Li; Pengfei Yu; Chunyue Wu; Xiaowen Wang; Meng Liu; Shuangsheng Deng; Chunming Guo; Ruirong Tan

doi:10.3389/fonc.2023.1064548

A deep learning model for drug screening and evaluation in bladder cancer organoids

Front Oncol. 2023 Apr 24:13:1064548. doi: 10.3389/fonc.2023.1064548. eCollection 2023.

Authors

Shudi Zhang¹, Lu Li², Pengfei Yu¹, Chunyue Wu², Xiaowen Wang¹, Meng Liu², Shuangsheng Deng², Chunming Guo², Ruirong Tan³

Affiliations

¹ School of Information Science and Engineering, Yunnan University, Kunming, China.
² College of Life Sciences, Yunnan University, Kunming, China.
³ Center for Organoids and Translational Pharmacology, Translational Chinese Medicine Key Laboratory of Sichuan Province, Sichuan Institute for Translational Chinese Medicine, Sichuan Academy of Chinese Medicine Sciences, Chengdu, China.

Abstract

Three-dimensional cell tissue culture, which produces biological structures termed organoids, has rapidly promoted the progress of biological research, including basic research, drug discovery, and regenerative medicine. However, due to the lack of algorithms and software, analysis of organoid growth is labor intensive and time-consuming. Currently it requires individual measurements using software such as ImageJ, leading to low screening efficiency when used for a high throughput screen. To solve this problem, we developed a bladder cancer organoid culture system, generated microscopic images, and developed a novel automatic image segmentation model, AU2Net (Attention and Cross U2Net). Using a dataset of two hundred images from growing organoids (day1 to day 7) and organoids with or without drug treatment, our model applies deep learning technology for image segmentation. To further improve the accuracy of model prediction, a variety of methods are integrated to improve the model's specificity, including adding Grouping Cross Merge (GCM) modules at the model's jump joints to strengthen the model's feature information. After feature information acquisition, a residual attentional gate (RAG) is added to suppress unnecessary feature propagation and improve the precision of organoids segmentation by establishing rich context-dependent models for local features. Experimental results show that each optimization scheme can significantly improve model performance. The sensitivity, specificity, and F1-Score of the ACU2Net model reached 94.81%, 88.50%, and 91.54% respectively, which exceed those of U-Net, Attention U-Net, and other available network models. Together, this novel ACU2Net model can provide more accurate segmentation results from organoid images and can improve the efficiency of drug screening evaluation using organoids.

Keywords: U2Net model; bladder cancer organoids; deep learning; drug screening; image segmentation.

Grants and funding

This paper is supported by the National Natural Science Foundation of China (62066046); National Natural Science Foundation of China, No.32070818; Key Project of Science and Technology Department of Yunnan Province, No.202001B050005; the National Natural Science Foundation of China (Grant No. 82204695); the Sichuan Provincial Research Institutes Basic Research Operations Fund Project (Grant No. A-2022N-Z-2); and the Sichuan Academy of Traditional Chinese Medicine Research Project (Grant No. QNCJRSC2022-9).