ArXiv Preprint
Usually, lesions are not isolated but are associated with the surrounding
tissues. For example, the growth of a tumour can depend on or infiltrate into
the surrounding tissues. Due to the pathological nature of the lesions, it is
challenging to distinguish their boundaries in medical imaging. However, these
uncertain regions may contain diagnostic information. Therefore, the simple
binarization of lesions by traditional binary segmentation can result in the
loss of diagnostic information. In this work, we introduce the image matting
into the 3D scenes and use the alpha matte, i.e., a soft mask, to describe
lesions in a 3D medical image. The traditional soft mask acted as a training
trick to compensate for the easily mislabelled or under-labelled ambiguous
regions. In contrast, 3D matting uses soft segmentation to characterize the
uncertain regions more finely, which means that it retains more structural
information for subsequent diagnosis and treatment. The current study of image
matting methods in 3D is limited. To address this issue, we conduct a
comprehensive study of 3D matting, including both traditional and
deep-learning-based methods. We adapt four state-of-the-art 2D image matting
algorithms to 3D scenes and further customize the methods for CT images to
calibrate the alpha matte with the radiodensity. Moreover, we propose the first
end-to-end deep 3D matting network and implement a solid 3D medical image
matting benchmark. Its efficient counterparts are also proposed to achieve a
good performance-computation balance. Furthermore, there is no high-quality
annotated dataset related to 3D matting, slowing down the development of
data-driven deep-learning-based methods. To address this issue, we construct
the first 3D medical matting dataset. The validity of the dataset was verified
through clinicians' assessments and downstream experiments.
Lin Wang, Xiufen Ye, Donghao Zhang, Wanji He, Lie Ju, Yi Luo, Huan Luo, Xin Wang, Wei Feng, Kaimin Song, Xin Zhao, Zongyuan Ge
2022-10-11
