Generative modeling of histology tissue reduces human annotation effort for segmentation model development

Brendon Lutnick; Nicholas Lucarelli; Pinaki Sarder

doi:10.1117/12.2655282

6 April 2023 Generative modeling of histology tissue reduces human annotation effort for segmentation model development

Brendon Lutnick, Nicholas Lucarelli, Pinaki Sarder

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124711Q (2023) https://doi.org/10.1117/12.2655282
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Segmentation of histology tissue whole side images is an important step for tissue analysis. Given enough annotated training data, modern neural networks are capable of accurate reproducible segmentation; however, the annotation of training datasets is time consuming. Techniques such as human-in-the-loop annotation attempt to reduce this annotation burden, but still require vast initial annotation. Semi-supervised learning—a technique which leverages both labeled and unlabeled data to learn features—has shown promise for easing the burden of annotation. Towards this goal, we employ a recently published semi-supervised method, datasetGAN, for the segmentation of glomeruli from renal biopsy images. We compare the performance of models trained using datasetGAN and traditional annotation and show that datasetGAN significantly reduces the amount of annotation required to develop a highly performing segmentation model. We also explore the usefulness of datasetGAN for transfer learning and find that this method greatly enhances the performance when a limited number of whole slide images are used for training.

Citation Download Citation

Brendon Lutnick, Nicholas Lucarelli, and Pinaki Sarder "Generative modeling of histology tissue reduces human annotation effort for segmentation model development", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124711Q (6 April 2023); https://doi.org/10.1117/12.2655282

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