Abstract
We present a segmentation software package primarily targeting medical and biological applications, with a high level of visual feedback and several usability enhancements over existing packages. Specifically, we provide a substantially faster GPU implementation of the local Gaussian distribution fitting energy model, which can segment inhomogeneous objects with poorly defined boundaries as often encountered in biomedical images. We also provide interactive brushes to guide the segmentation process in a semiautomated framework.The speed of our implementation allows us to visualize the active surface in real time with a built-in ray tracer, where users may halt evolution at any time step to correct implausible segmentation by painting new blocking regions or new seeds. Quantitative and qualitative validation is presented, demonstrating the practical efficacy of our interactive elements for a variety of real-world datasets.
Highlights
We provide qualitative results to justify the utility of our interactive brushes and assess the segmentation of real-world images from various domains
These results show the Graphics processing units (GPUs) to be near-identical to the CPU implementation; we find small discrepancies at the boundary at sub-voxel precision caused by different implementations of low-level math library functions and different algebra in the intermediate steps (Eqs. 11 and 12)
We have shown that sophisticated level set segmentation energy models, with sequential dependencies among intermediate processing steps, can be implemented efficiently on the GPU through careful structuring of the GPU kernels within the constraints of the GPU memory architecture
Summary
The primary problems with current segmentation approaches are that they are either: (1) too limited, e.g., only able to segment objects by simple criteria, such as objects with consistent mean intensity [18, 36], (2) using too much memory or too slow, taking several hours to segment large 2D or 3D objects [47], (3) lacking in interactivity with the segmentation process in response to visual feedback [54], (4) requiring copious training data [22], or (5) difficult to use, requiring large interfaces, and multiple algorithms [50].
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