Accurate segmentation is essential for echocardiography-based assessment of
cardiovascular diseases (CVDs). However, the variability among sonographers and
the inherent challenges of ultrasound images hinder precise segmentation. By
leveraging the joint representation of image and text modalities,
Vision-Language Segmentation Models (VLSMs) can incorporate rich contextual
information, potentially aiding in accurate and explainable segmentation.
However, the lack of readily available data in echocardiography hampers the
training of VLSMs. In this study, we explore using synthetic datasets from
Semantic Diffusion Models (SDMs) to enhance VLSMs for echocardiography
segmentation. We evaluate results for two popular VLSMs (CLIPSeg and CRIS)
using seven different kinds of language prompts derived from several
attributes, automatically extracted from echocardiography images, segmentation
masks, and their metadata. Our results show improved metrics and faster
convergence when pretraining VLSMs on SDM-generated synthetic images before
finetuning on real images. The code, configs, and prompts are available at
this https URL

通过使用合成数据集和语义扩散模型，提高了视觉 - 语言分割模型在超声心动图分割中的性能。

合成推进：在超声心动图中利用合成数据增强视觉语言分割

Synthetic Boost: Leveraging Synthetic Data for Enhanced Vision-Language  Segmentation in Echocardiography

Lung cancer has been one of the leading causes of cancer-related deaths
worldwide for years. With the emergence of deep learning, computer-assisted
diagnosis (CAD) models based on learning algorithms can accelerate the nodule
screening process, providing valuable assis- tance to radiologists in their
daily clinical workflows. However, developing such robust and accurate models
often requires large-scale and diverse medical datasets with high-quality
annotations. Generating synthetic data provides a pathway for augmenting
datasets at a larger scale. Therefore, in this paper, we explore the use of
Semantic Diffusion Mod- els (SDM) to generate high-fidelity pulmonary CT images
from segmentation maps. We utilize annotation information from the LUNA16
dataset to create paired CT images and masks, and assess the quality of the
generated images using the Frechet Inception Distance (FID), as well as on two
common clinical downstream tasks: nodule detection and nodule localization.
Achieving improvements of 3.96% for detection accuracy and 8.50% for AP50 in
nodule localization task, respectively, demonstrates the feasibility of the
approach.

利用语义扩散模型生成高保真度肺部 CT 图像扩充数据集，并在肺结节检测和定位任务中展现了优异性能。