This paper explores the segmentation of very small medical objects with
significant clinical value. While Convolutional Neural Networks (CNNs),
particularly UNet-like models, and recent Transformers have shown substantial
progress in image segmentation, our empirical findings reveal their poor
performance in segmenting the small medical objects and lesions concerned in
this paper. This limitation may be attributed to information loss during their
encoding and decoding process. In response to this challenge, we propose a
novel model named EFCNet for small object segmentation in medical images. Our
model incorporates two modules: the Cross-Stage Axial Attention Module (CSAA)
and the Multi-Precision Supervision Module (MPS). These modules address
information loss during encoding and decoding procedures, respectively.
Specifically, CSAA integrates features from all stages of the encoder to
adaptively learn suitable information needed in different decoding stages,
thereby reducing information loss in the encoder. On the other hand, MPS
introduces a novel multi-precision supervision mechanism to the decoder. This
mechanism prioritizes attention to low-resolution features in the initial
stages of the decoder, mitigating information loss caused by subsequent
convolution and sampling processes and enhancing the model's global perception.
We evaluate our model on two benchmark medical image datasets. The results
demonstrate that EFCNet significantly outperforms previous segmentation methods
designed for both medical and normal images.

本文研究了在具有临床价值的非常小的医疗对象的分割问题。我们发现，卷积神经网络（CNNs）和最近的 Transformer 在图像分割方面取得了重大进展，但在分割本文中涉及的小型医疗对象和病变方面表现不佳。为了应对这一挑战，我们提出了一种名为 EFCNet 的新模型，用于医学图像中的小物体分割。我们的模型包括两个模块：交叉阶段轴向注意力模块（CSAA）和多精度监督模块（MPS）。通过这些模块，我们解决了编码和解码过程中的信息损失问题。 CSAA 从编码器的所有阶段集成特征，以自适应地学习不同解码阶段所需的适当信息，从而减少编码器中的信息损失。另一方面，MPS 向解码器引入了一种新颖的多精度监督机制。该机制在解码器的初始阶段优先关注低分辨率特征，减轻了后续卷积和采样过程导致的信息损失，并增强了模型的全局感知能力。我们在两个基准医学图像数据集上评估了我们的模型。结果表明，EFCNet 明显优于之前设计用于医学和普通图像的分割方法。

EFCNet：对小型医学物体分割，每个特征都至关重要

EFCNet: Every Feature Counts for Small Medical Object Segmentation

White Matter Hyperintensities (WMH) are areas of the brain that have higher
intensity than other normal brain regions on Magnetic Resonance Imaging (MRI)
scans. WMH is often associated with small vessel disease in the brain, making
early detection of WMH important. However, there are two common issues in the
detection of WMH: high ambiguity and difficulty in detecting small WMH. In this
study, we propose a method called Probabilistic TransUNet to address the
precision of small object segmentation and the high ambiguity of medical
images. To measure model performance, we conducted a k-fold cross validation
and cross dataset robustness experiment. Based on the experiments, the addition
of a probabilistic model and the use of a transformer-based approach were able
to achieve better performance.

本研究提出了一种名为 Probabilistic TransUNet 的方法来解决医学图像中小物体分割的准确性和模糊性问题，并通过 k 折交叉验证和跨数据集鲁棒性实验表明加入概率模型和使用基于 Transformer 的方法可以取得更好的性能。