MorphoSeg: An Uncertainty-Aware Deep Learning Method for Biomedical Segmentation of Complex Cellular Morphologies

• Main Authors: Zhang, Tianhao, McCourty, Heather J, Sanchez-Tafolla, Berardo M, Nikolaev, Anton, Mihaylova, Lyudmila S
• Format: Journal Article
• Language: English
• Published: 25-09-2024
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2409.17110

Deep learning has revolutionized medical and biological imaging, particularly
in segmentation tasks. However, segmenting biological cells remains challenging
due to the high variability and complexity of cell shapes. Addressing this
challenge requires high-quality datasets that accurately represent the diverse
morphologies found in biological cells. Existing cell segmentation datasets are
often limited by their focus on regular and uniform shapes. In this paper, we
introduce a novel benchmark dataset of Ntera-2 (NT2) cells, a pluripotent
carcinoma cell line, exhibiting diverse morphologies across multiple stages of
differentiation, capturing the intricate and heterogeneous cellular structures
that complicate segmentation tasks. To address these challenges, we propose an
uncertainty-aware deep learning framework for complex cellular morphology
segmentation (MorphoSeg) by incorporating sampling of virtual outliers from
low-likelihood regions during training. Our comprehensive experimental
evaluations against state-of-the-art baselines demonstrate that MorphoSeg
significantly enhances segmentation accuracy, achieving up to a 7.74% increase
in the Dice Similarity Coefficient (DSC) and a 28.36% reduction in the
Hausdorff Distance. These findings highlight the effectiveness of our dataset
and methodology in advancing cell segmentation capabilities, especially for
complex and variable cell morphologies. The dataset and source code is publicly
available at https://github.com/RanchoGoose/MorphoSeg.