ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images

By Paul Yushkevich, Yang Gao, Guido Gerig. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016

641 citations recorded by the source corpus.

Obtaining quantitative measures from biomedical images often requires segmentation, i.e., finding and outlining the structures of interest. Multi-modality imaging datasets, in which multiple imaging measures are available at each spatial location, are increasingly common, particularly in MRI. In applications where fully automatic segmentation algorithms are unavailable or fail to perform at desired levels of accuracy, semi-automatic segmentation can be a time-saving alternative to manual segmentation, allowing the human expert to guide segmentation, while minimizing the effort expended by the expert on repetitive tasks that can be automated. However, few existing 3D image analysis tools support semi-automatic segmentation of multi-modality imaging data. This paper describes new extensions to the ITK-SNAP interactive image visualization and segmentation tool that support semi-automatic segmentation of multi-modality imaging datasets in a way that utilizes information from all available modalities simultaneously. The approach combines Random Forest classifiers, trained by the user by placing several brushstrokes in the image, with the active contour segmentation algorithm. The new multi-modality semi-automatic segmentation approach is evaluated in the context of high-grade glioblastoma segmentation.

Subfields: Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a8cfdd8da19aa68afb1dbe51c7af80414da4d2e6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Feifei Li
2. Jun Ma
3. Bo Wang

In Computer Vision and Pattern Recognition

1. Mukhriddin Mukhiddinov
2. Akmalbek Bobomirzaevich Abdusalomov
3. Feifei Li
4. Jun Ma
5. Bo Wang
6. T. Whangbo

Citations in the corpus

• U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation (2024, methodology): “The annotations were generated by radiologists with the assistance of MedSAM [32] and ITK-SNAP [48].”
• Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging (2023, background): “Additionally, the authors presented a new semi-automatic segmentation approach for images of brain tumors [32].”