Uncertainty quantification in medical images has become an essential addition
to segmentation models for practical application in the real world. Although
there are valuable developments in accurate uncertainty quantification methods
using 2D images and slices of 3D volumes, in clinical practice, the complete 3D
volumes (such as CT and MRI scans) are used to evaluate and plan the medical
procedure. As a result, the existing 2D methods miss the rich 3D spatial
information when resolving the uncertainty. A popular approach for quantifying
the ambiguity in the data is to learn a distribution over the possible
hypotheses. In recent work, this ambiguity has been modeled to be strictly
Gaussian. Normalizing Flows (NFs) are capable of modelling more complex
distributions and thus, better fit the embedding space of the data. To this
end, we have developed a 3D probabilistic segmentation framework augmented with
NFs, to enable capturing the distributions of various complexity. To test the
proposed approach, we evaluate the model on the LIDC-IDRI dataset for lung
nodule segmentation and quantify the aleatoric uncertainty introduced by the
multi-annotator setting and inherent ambiguity in the CT data. Following this
approach, we are the first to present a 3D Squared Generalized Energy Distance
(GED) of 0.401 and a high 0.468 Hungarian-matched 3D IoU. The obtained results
reveal the value in capturing the 3D uncertainty, using a flexible posterior
distribution augmented with a Normalizing Flow. Finally, we present the
aleatoric uncertainty in a visual manner with the aim to provide clinicians
with additional insight into data ambiguity and facilitating more informed
decision-making.

该研究通过引入 Normalizing Flows 技术，提出了一种基于 3D volumes 的概率分割框架，并在 CT 数据上进行了评估，结果表明该方法可以更准确地捕捉到 3D 空间信息的不确定性，同时以直观的方式提供了不确定性信息，有助于医生做出更明智的决策。

全三维医学数据中基于概率的三维分割及其不确定性量化

Probabilistic 3D segmentation for aleatoric uncertainty quantification  in full 3D medical data

Existing color sampling based alpha matting methods use the compositing
equation to estimate alpha at a pixel from pairs of foreground (F) and
background (B) samples. The quality of the matte depends on the selected (F,B)
pairs. In this paper, the matting problem is reinterpreted as a sparse coding
of pixel features, wherein the sum of the codes gives the estimate of the alpha
matte from a set of unpaired F and B samples. A non-parametric probabilistic
segmentation provides a certainty measure on the pixel belonging to foreground
or background, based on which a dictionary is formed for use in sparse coding.
By removing the restriction to conform to (F,B) pairs, this method allows for
better alpha estimation from multiple F and B samples. The same framework is
extended to videos, where the requirement of temporal coherence is handled
effectively. Here, the dictionary is formed by samples from multiple frames. A
multi-frame graph model, as opposed to a single image as for image matting, is
proposed that can be solved efficiently in closed form. Quantitative and
qualitative evaluations on a benchmark dataset are provided to show that the
proposed method outperforms current state-of-the-art in image and video
matting.

本研究提出了一种基于稀疏编码、非参数概率分割和字典学习的无配对前景背景样本的 alpha 合成方法，可以从多个前景和背景样本中更准确地估计 alpha 值，同时在视频 matting 中也表现出较好的性能。