MedSegBench: A Comprehensive Benchmark for Medical Image Segmentation in Diverse Data Modalities

Abstract

ABSTRACTMedSegBench is a comprehensive benchmark designed to evaluate deep learning models for medical image segmentation across a wide range of modalities.. This benchmark includes 35 datasets with over 60,000 images, covering modalities such as ultrasound, MRI, and X-ray. It addresses challenges in medical imaging, such as variability in image quality and dataset imbalances, by providing standardized datasets with train/validation/test splits. The benchmark supports binary and multi-class segmentation tasks with up to 19 classes. Evaluations are conducted using the U-Net architecture with various encoder/decoder networks, including ResNets, EfficientNet, and DenseNet, to evaluate model performance. MedSegBench serves as a valuable resource for developing robust and flexible segmentation algorithms. It allows for fair comparisons across different models and promotes the development of universal models for medical tasks. The datasets and source code are publicly available, encouraging further research and development in medical image analysis.