Purpose: Body composition measurements from routine abdominal CT can yield
personalized risk assessments for asymptomatic and diseased patients. In
particular, attenuation and volume measures of muscle and fat are associated
with important clinical outcomes, such as cardiovascular events, fractures, and
death. This study evaluates the reliability of an Internal tool for the
segmentation of muscle and fat (subcutaneous and visceral) as compared to the
well-established public TotalSegmentator tool.
Methods: We assessed the tools across 900 CT series from the publicly
available SAROS dataset, focusing on muscle, subcutaneous fat, and visceral
fat. The Dice score was employed to assess accuracy in subcutaneous fat and
muscle segmentation. Due to the lack of ground truth segmentations for visceral
fat, Cohen's Kappa was utilized to assess segmentation agreement between the
tools.
Results: Our Internal tool achieved a 3% higher Dice (83.8 vs. 80.8) for
subcutaneous fat and a 5% improvement (87.6 vs. 83.2) for muscle segmentation
respectively. A Wilcoxon signed-rank test revealed that our results were
statistically different with p<0.01. For visceral fat, the Cohen's kappa score
of 0.856 indicated near-perfect agreement between the two tools. Our internal
tool also showed very strong correlations for muscle volume (R^2=0.99), muscle
attenuation (R^2=0.93), and subcutaneous fat volume (R^2=0.99) with a moderate
correlation for subcutaneous fat attenuation (R^2=0.45).
Conclusion: Our findings indicated that our Internal tool outperformed
TotalSegmentator in measuring subcutaneous fat and muscle. The high Cohen's
Kappa score for visceral fat suggests a reliable level of agreement between the
two tools. These results demonstrate the potential of our tool in advancing the
accuracy of body composition analysis.

通过比较我们的内部工具和 TotalSegmentator 的测量结果，研究表明我们的工具在测量皮下脂肪和肌肉方面表现优于 TotalSegmentator，并且在内脏脂肪的分割方面两种工具之间存在可靠的一致性。这些结果显示了我们的工具在提高人体组成分析的准确性方面的潜力。

增强的 CT 图像肌肉和脂肪分割用于身体构成分析：一项比较研究

Enhanced Muscle and Fat Segmentation for CT-Based Body Composition  Analysis: A Comparative Study

As a new emerging and promising type of generative models, diffusion models
have proven to outperform Generative Adversarial Networks (GANs) in multiple
tasks, including image synthesis. In this work, we explore semantic image
synthesis for abdominal CT using conditional diffusion models, which can be
used for downstream applications such as data augmentation. We systematically
evaluated the performance of three diffusion models, as well as to other
state-of-the-art GAN-based approaches, and studied the different conditioning
scenarios for the semantic mask. Experimental results demonstrated that
diffusion models were able to synthesize abdominal CT images with better
quality. Additionally, encoding the mask and the input separately is more
effective than na\"ive concatenating.

我们研究了基于条件扩散模型的腹部 CT 语义图像合成，并对三种不同的扩散模型以及其他基于生成对抗网络的方法进行了系统评估。实验结果表明，扩散模型能够合成具有更好质量的腹部 CT 图像。此外，将语义掩码和输入分别编码比简单拼接更加有效。

腹部 CT 的语义图像合成

Semantic Image Synthesis for Abdominal CT

Nonlinear image registration continues to be a fundamentally important tool
in medical image analysis. Diagnostic tasks, image-guided surgery and
radiotherapy as well as motion analysis all rely heavily on accurate
intra-patient alignment. Furthermore, inter-patient registration enables
atlas-based segmentation or landmark localisation and shape analysis. When
labelled scans are scarce and anatomical differences large, conventional
registration has often remained superior to deep learning methods that have so
far mainly dealt with relatively small or low-complexity deformations. We
address this shortcoming by leveraging ideas from probabilistic dense
displacement optimisation that has excelled in many registration tasks with
large deformations. We propose to design a network with approximate
min-convolutions and mean field inference for differentiable displacement
regularisation within a discrete weakly-supervised registration setting. By
employing these meaningful and theoretically proven constraints, our learnable
registration algorithm contains very few trainable weights (primarily for
feature extraction) and is easier to train with few labelled scans. It is very
fast in training and inference and achieves state-of-the-art accuracies for the
challenging inter-patient registration of abdominal CT outperforming previous
deep learning approaches by 15% Dice overlap.

提出了一种基于概率密度优化和深度学习的非线性医学图像配准方法，该方法通过近似最小卷积和平均场推断进行可微位移正则化，在少量标记的扫描中可实现较高准确度。