  Medical imaging segmentation is a highly active area of research, with deep
learning-based methods achieving state-of-the-art results in several
benchmarks. However, the lack of standardized tools for training, testing, and
evaluating new methods makes the comparison of methods difficult. To address
this, we introduce the Medical Imaging Segmentation Toolkit (MIST), a simple,
modular, and end-to-end medical imaging segmentation framework designed to
facilitate consistent training, testing, and evaluation of deep learning-based
medical imaging segmentation methods. MIST standardizes data analysis,
preprocessing, and evaluation pipelines, accommodating multiple architectures
and loss functions. This standardization ensures reproducible and fair
comparisons across different methods. We detail MIST's data format
requirements, pipelines, and auxiliary features and demonstrate its efficacy
using the BraTS Adult Glioma Post-Treatment Challenge dataset. Our results
highlight MIST's ability to produce accurate segmentation masks and its
scalability across multiple GPUs, showcasing its potential as a powerful tool
for future medical imaging research and development.
