We are excited to announce our latest publication, titled “OrgaSegment: Deep-Learning Based Organoid Segmentation to Quantify CFTR Dependent Fluid Secretion.”

Epithelial ion and fluid transport studies in patient-derived organoids (PDOs) play a crucial role in preclinical research, drug development, and precision medicine. These studies are particularly important for understanding and treating diseases like cystic fibrosis (CF). By analyzing changes in organoid size, researchers can assess the fluid transport properties of PDOs.

OrgaSegment, our innovative MASK-RCNN based deep-learning model, offers a breakthrough in this field. It segments intestinal PDOs from bright-field images, accurately recognizing both spherical and irregular CF organoids. This advanced tool enables precise quantification of organoid swelling, differentiation between various CFTR mutations, and evaluation of drug responses.

With OrgaSegment, researchers can significantly enhance their studies on CFTR-based fluid secretion and other epithelial ion transport mechanisms. This model not only improves the accuracy of fluid secretion measurements but also supports the development of more effective treatments for cystic fibrosis and other related conditions.

Discover the full details of our research and explore the potential of OrgaSegment in our latest publication.