MIScnn

MIScnn implements a Python framework for developing and evaluating convolutional neural network (CNN) and deep learning-based medical image segmentation pipelines.

Key Features:

• Comprehensive Pipeline Components: Implements data input/output (I/O), preprocessing, data augmentation, patch-wise analysis, and evaluation metrics for medical image segmentation.
• Models: Includes a curated collection of deep learning architectures, including 3D U-Net, with demonstrated use in the Kidney Tumor Segmentation Challenge 2019.
• Full Pipeline Customization: Provides configurable pipeline components and open interfaces to tailor preprocessing, model training, and inference parameters.
• Automated Evaluation: Supports automated evaluation workflows, including cross-validation, for rigorous model assessment across datasets.

Scientific Applications:

• Multi-class semantic segmentation: Enables multi-class semantic segmentation of volumetric medical imaging datasets.
• Kidney tumor segmentation: Has been applied to kidney tumor segmentation as demonstrated in the Kidney Tumor Segmentation Challenge 2019.

Methodology:

Integrates data preprocessing, data augmentation, patch-wise analysis, convolutional neural networks (e.g., 3D U-Net), and automated evaluation including cross-validation.