Accurate segmentation of multiple organs in Computed Tomography (CT) images
plays a vital role in computer-aided diagnosis systems. Various
supervised-learning approaches have been proposed recently. However, these
methods heavily depend on a large amount of high-quality labeled data, which is
expensive to obtain in practice. In this study, we present a label-efficient
learning approach using a pre-trained diffusion model for multi-organ
segmentation tasks in CT images. First, a denoising diffusion model was trained
using unlabeled CT data, generating additional two-dimensional (2D) CT images.
Then the pre-trained denoising diffusion network was transferred to the
downstream multi-organ segmentation task, effectively creating a
semi-supervised learning model that requires only a small amount of labeled
data. Furthermore, linear classification and fine-tuning decoder strategies
were employed to enhance the network's segmentation performance. Our generative
model at 256x256 resolution achieves impressive performance in terms of
Fr\'echet inception distance, spatial Fr\'echet inception distance, and
F1-score, with values of 11.32, 46.93, and 73.1\%, respectively. These results
affirm the diffusion model's ability to generate diverse and realistic 2D CT
images. Additionally, our method achieves competitive multi-organ segmentation
performance compared to state-of-the-art methods on the FLARE 2022 dataset,
particularly in limited labeled data scenarios. Remarkably, even with only 1\%
and 10\% labeled data, our method achieves Dice similarity coefficients (DSCs)
of 71.56\% and 78.51\% after fine-tuning, respectively. The method achieves a
DSC score of 51.81\% using just four labeled CT scans. These results
demonstrate the efficacy of our approach in overcoming the limitations of
supervised learning heavily reliant on large-scale labeled data.

本研究提出了一种使用预训练扩散模型进行 CT 图像的多器官分割任务的标签高效学习方法，该方法仅需要少量标记数据，在有限标记数据场景下取得了竞争性的分割性能。

标签高效的多器官分割方法与扩散模型

Label-efficient Multi-organ Segmentation Method with Diffusion Model

Segmentation of the bronchovascular bundle within the lung parenchyma is a
key step for the proper analysis and planning of many pulmonary diseases. It
might also be considered the preprocessing step when the goal is to segment the
nodules from the lung parenchyma. We propose a segmentation pipeline for the
bronchovascular bundle based on the Computed Tomography images, returning
either binary or labelled masks of vessels and bronchi situated in the lung
parenchyma. The method consists of two modules, modeling of the bronchial tree
and vessels. The core revolves around a similar pipeline, the determination of
the initial perimeter by the GMM method, skeletonization, and hierarchical
analysis of the created graph. We tested our method on both low-dose CT and
standard-dose CT, with various pathologies, reconstructed with various slice
thicknesses, and acquired from various machines. We conclude that the method is
invariant with respect to the origin and parameters of the CT series. Our
pipeline is best suited for studies with healthy patients, patients with lung
nodules, and patients with emphysema.

基于计算机断层扫描图像的细支气管和血管束分割管道，用于肺实质的适当分析和规划以及肺实质中结节的分割。

BRONCO：使用计算机断层扫描图像自动建模支气管血管束

BRONCO: Automated modelling of the bronchovascular bundle using the  Computed Tomography Images

The accurate diagnosis on pathological subtypes for lung cancer is of
significant importance for the follow-up treatments and prognosis managements.
In this paper, we propose self-generating hybrid feature network (SGHF-Net) for
accurately classifying lung cancer subtypes on computed tomography (CT) images.
Inspired by studies stating that cross-scale associations exist in the image
patterns between the same case's CT images and its pathological images, we
innovatively developed a pathological feature synthetic module (PFSM), which
quantitatively maps cross-modality associations through deep neural networks,
to derive the "gold standard" information contained in the corresponding
pathological images from CT images. Additionally, we designed a radiological
feature extraction module (RFEM) to directly acquire CT image information and
integrated it with the pathological priors under an effective feature fusion
framework, enabling the entire classification model to generate more indicative
and specific pathologically related features and eventually output more
accurate predictions. The superiority of the proposed model lies in its ability
to self-generate hybrid features that contain multi-modality image information
based on a single-modality input. To evaluate the effectiveness, adaptability,
and generalization ability of our model, we performed extensive experiments on
a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to
compare our model and a series of state-of-the-art (SOTA) classification
models. The experimental results demonstrated the superiority of our model for
lung cancer subtypes classification with significant accuracy improvements in
terms of accuracy (ACC), area under the curve (AUC), and F1 score.

提出了自动生成混合特征网络（SGHF-Net）用于肺癌亚型的模式分类，通过深度神经网络 quantitatively maps cross-modality associations 从 CT images 提取 “gold standard” 信息，实现了更准确的预测，并在多中心数据集上展示了该模型的卓越性。