Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Image segmentation is the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. Image segmentation has been used for various applications, including locating tumors and other pathologies, measuring tissue volumes, diagnosis and study of anatomical structure, surgery planning, virtual surgery simulation, and intra-surgery navigation.
Image segmentation may be solved as a classification problem. Learning networks, such as Convolutional Neural Network (CNN) with powerful hierarchical architectures, have been applied to image segmentation to improve accuracy. For example, automatic classifications using CNN could significantly outperform conventional image segmentation methods, such as atlas-based segmentation, and shape-based segmentation.
Different medical imaging modalities have been used for obtaining medical images. For example, Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. Each imaging modality has its own advantages and disadvantages. For example, MRI is good at imaging soft tissue and showing tissue difference between normal and abnormal. For example, MRI is generally more sensitive in detecting brain abnormalities during the early stages of disease, and is particularly useful in detecting white matter disease, such as multiple sclerosis, progressive multifocal leukoencephalopathy, leukodystrophy, and post-infectious encephalitis. However, CT is good at imaging bone, lungs and blood vessels with contrast agent.
Due to the strengths of different imaging modalities, multi-modality image segmentation provides improved accuracy because fusion of different modalities could provide complimentary information.
Embodiments of the disclosure address the above problems by systems and methods for segmenting a single modality image using a learning network that leverages multi-modality information during training stage.