Abstract 230: Deep learning embedding-based segmentation for morphological analysis in organoids

Abstract

Abstract Background: The organoid model is a useful tool for modeling the cellular microenvironment of the organ from which they are derived. Organoids recapitulate the self-organization of heterogenous cell types and the microenvironment. Quantifying the morphological features of organoids can provide valuable insights into cellular organizational defects and growth characteristics, which can facilitate drug discovery. Measurement of these characteristics in live organoids can be performed quickly and easily directly in culture using widefield microscopy. The current state-of-the-art method to detect and identify the shape of individual organoids in brightfield images uses the U-Net Convolutional Neural Network (CNN) with a watershed transform to label pixels. However, this method yields jagged shape proposals and cannot detect overlapping regions. We propose the use of an embedding-based segmentation network based on the Branched ERF-Net to solve these issues. Methods: Organoids were established from the proximal colon of BrafV600E heterozygous mice, and 50 phase-contrast images of the organoids were acquired at 2.5x and 4x magnification. Organoids present in acquired images were labeled manually. The network was trained on this collection of manually labeled images and validated on a separate validation dataset. Results: The network accurately labels organoids. Overlapping organoids are segmented correctly and shape proposals are smoother. Detection of overlapping organoids with the Branched ERF-Net architecture yields an accurate organoid count, verified against manual counting. Smoother shape proposals also enable the use of convexity defects to measure organoid budding. Conclusions: Deep-learning analysis of widefield images enables the rapid assessment of morphological characteristics, such as size, count, and budding, which are key to understanding proliferation and differentiation changes. The modified Branched ERF-Net architecture we propose for organoid segmentation is a robust and versatile method to quantify organoid morphology. Citation Format: Akash Sureshkumar, Shilpa Bisht, Hariharan Easwaran. Deep learning embedding-based segmentation for morphological analysis in organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 230.