Medical image segmentation is crucial for disease diagnosis and monitoring.
Though effective, the current segmentation networks such as UNet struggle with
capturing long-range features. More accurate models such as TransUNet,
Swin-UNet, and CS-UNet have higher computation complexity. To address this
problem, we propose GCtx-UNet, a lightweight segmentation architecture that can
capture global and local image features with accuracy better or comparable to
the state-of-the-art approaches. GCtx-UNet uses vision transformer that
leverages global context self-attention modules joined with local
self-attention to model long and short range spatial dependencies. GCtx-UNet is
evaluated on the Synapse multi-organ abdominal CT dataset, the ACDC cardiac MRI
dataset, and several polyp segmentation datasets. In terms of Dice Similarity
Coefficient (DSC) and Hausdorff Distance (HD) metrics, GCtx-UNet outperformed
CNN-based and Transformer-based approaches, with notable gains in the
segmentation of complex and small anatomical structures. Moreover, GCtx-UNet is
much more efficient than the state-of-the-art approaches with smaller model
size, lower computation workload, and faster training and inference speed,
making it a practical choice for clinical applications.

提出了一种轻量级分割架构 GCtx-UNet，可以捕获全局和局部图像特征，其精度优于或与最先进方法相媲美。GCtx-UNet 在多器官腹部 CT 数据集、ACDC 心脏 MRI 数据集和多个息肉分割数据集上进行了评估，在 Dice 相似系数和 Hausdorff 距离指标方面，超过了基于 CNN 和 Transformer 的方法，在复杂和小型解剖结构的分割中取得显著收益。此外，GCtx-UNet 比最先进的方法更高效，模型尺寸更小，计算负载更低，训练和推理速度更快，使其成为临床应用的实用选择。

GCtx-UNet：高效用于医学图像分割的网络

GCtx-UNet: Efficient Network for Medical Image Segmentation

Imaging mass cytometry (IMC) is a relatively new technique for imaging
biological tissue at subcellular resolution. In recent years, learning-based
segmentation methods have enabled precise quantification of cell type and
morphology, but typically rely on large datasets with fully annotated ground
truth (GT) labels. This paper explores the effects of imperfect labels on
learning-based segmentation models and evaluates the generalisability of these
models to different tissue types. Our results show that removing 50% of cell
annotations from GT masks only reduces the dice similarity coefficient (DSC)
score to 0.874 (from 0.889 achieved by a model trained on fully annotated GT
masks). This implies that annotation time can in fact be reduced by at least
half without detrimentally affecting performance. Furthermore, training our
single-tissue model on imperfect labels only decreases DSC by 0.031 on an
unseen tissue type compared to its multi-tissue counterpart, with negligible
qualitative differences in segmentation. Additionally, bootstrapping the
worst-performing model (with 5% of cell annotations) a total of ten times
improves its original DSC score of 0.720 to 0.829. These findings imply that
less time and work can be put into the process of producing comparable
segmentation models; this includes eliminating the need for multiple IMC tissue
types during training, whilst also providing the potential for models with very
few labels to improve on themselves. Source code is available on GitHub:
this https URL

通过去除细胞注释中的 50% ，学习性的分割模型表现的结果只比完整的标签相应的模型降低了 DSC 指标，表明可以至少减少一半的注释时间而不影响性能，并且单一组织模型与多组织模型在新颖组织类型上的性能差异微乎其微，并且通过多次增加最差性能模型的训练可以提高其性能。

推动细胞分割模型在成像质谱细胞学中的极限

Pushing the limits of cell segmentation models for imaging mass  cytometry

Segment anything model (SAM) has revolutionized natural image segmentation,
but its performance on medical images is limited. This work presents MedSAM,
the first attempt at extending the success of SAM to medical images, with the
goal of creating a universal tool for the segmentation of various medical
targets. Specifically, we first curate a large-scale medical image dataset,
encompassing over 200,000 masks across 11 different modalities. Then, we
develop a simple fine-tuning method to adapt SAM to general medical image
segmentation. Comprehensive experiments on 21 3D segmentation tasks and 9 2D
segmentation tasks demonstrate that MedSAM outperforms the default SAM model
with an average Dice Similarity Coefficient (DSC) of 22.5% and 17.6% on 3D and
2D segmentation tasks, respectively. The code and trained model are publicly
available at https://github.com/bowang-lab/MedSAM.

MedSAM 是第一次尝试将 SAM 的成功延伸到医学图像中，通过构建一个包含超过 200,000 个掩膜的大规模医学图像数据集，并开发了一种简单的微调方法，以适应普通医学图像分割，在 21 个 3D 分割任务和 9 个 2D 分割任务的全面实验中，展示了超过默认 SAM 模型的平均 Dice 相似系数 (DSC) 分别达到了 22.5% 和 17.6 %。