31 research outputs found
The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models
The segmentation of the retinal vasculature from eye fundus images represents
one of the most fundamental tasks in retinal image analysis. Over recent years,
increasingly complex approaches based on sophisticated Convolutional Neural
Network architectures have been slowly pushing performance on well-established
benchmark datasets. In this paper, we take a step back and analyze the real
need of such complexity. Specifically, we demonstrate that a minimalistic
version of a standard U-Net with several orders of magnitude less parameters,
carefully trained and rigorously evaluated, closely approximates the
performance of current best techniques. In addition, we propose a simple
extension, dubbed W-Net, which reaches outstanding performance on several
popular datasets, still using orders of magnitude less learnable weights than
any previously published approach. Furthermore, we provide the most
comprehensive cross-dataset performance analysis to date, involving up to 10
different databases. Our analysis demonstrates that the retinal vessel
segmentation problem is far from solved when considering test images that
differ substantially from the training data, and that this task represents an
ideal scenario for the exploration of domain adaptation techniques. In this
context, we experiment with a simple self-labeling strategy that allows us to
moderately enhance cross-dataset performance, indicating that there is still
much room for improvement in this area. Finally, we also test our approach on
the Artery/Vein segmentation problem, where we again achieve results
well-aligned with the state-of-the-art, at a fraction of the model complexity
in recent literature. All the code to reproduce the results in this paper is
released
3D Matting: A Soft Segmentation Method Applied in Computed Tomography
Three-dimensional (3D) images, such as CT, MRI, and PET, are common in
medical imaging applications and important in clinical diagnosis. Semantic
ambiguity is a typical feature of many medical image labels. It can be caused
by many factors, such as the imaging properties, pathological anatomy, and the
weak representation of the binary masks, which brings challenges to accurate 3D
segmentation. In 2D medical images, using soft masks instead of binary masks
generated by image matting to characterize lesions can provide rich semantic
information, describe the structural characteristics of lesions more
comprehensively, and thus benefit the subsequent diagnoses and analyses. In
this work, we introduce image matting into the 3D scenes to describe the
lesions in 3D medical images. The study of image matting in 3D modality is
limited, and there is no high-quality annotated dataset related to 3D matting,
therefore slowing down the development of data-driven deep-learning-based
methods. To address this issue, we constructed the first 3D medical matting
dataset and convincingly verified the validity of the dataset through quality
control and downstream experiments in lung nodules classification. We then
adapt the four selected state-of-the-art 2D image matting algorithms to 3D
scenes and further customize the methods for CT images. Also, we propose the
first end-to-end deep 3D matting network and implement a solid 3D medical image
matting benchmark, which will be released to encourage further research.Comment: 12 pages, 7 figure