Medical image segmentation plays an important role in many image-guided
clinical approaches. However, existing segmentation algorithms mostly rely on
the availability of fully annotated images with pixel-wise annotations for
training, which can be both labor-intensive and expertise-demanding, especially
in the medical imaging domain where only experts can provide reliable and
accurate annotations. To alleviate this challenge, there has been a growing
focus on developing segmentation methods that can train deep models with weak
annotations, such as image-level, bounding boxes, scribbles, and points. The
emergence of vision foundation models, notably the Segment Anything Model
(SAM), has introduced innovative capabilities for segmentation tasks using weak
annotations for promptable segmentation enabled by large-scale pre-training.
Adopting foundation models together with traditional learning methods has
increasingly gained recent interest research community and shown potential for
real-world applications. In this paper, we present a comprehensive survey of
recent progress on annotation-efficient learning for medical image segmentation
utilizing weak annotations before and in the era of foundation models.
Furthermore, we analyze and discuss several challenges of existing approaches,
which we believe will provide valuable guidance for shaping the trajectory of
foundational models to further advance the field of medical image segmentation.

医学图像分割的注释高效学习与基础模型的挑战：综述与分析。

医学影像分割：从传统模型到基础模型的超越像素级监督

Beyond Pixel-Wise Supervision for Medical Image Segmentation: From  Traditional Models to Foundation Models

Nucleus instance segmentation from histopathology images suffers from the
extremely laborious and expert-dependent annotation of nucleus instances. As a
promising solution to this task, annotation-efficient deep learning paradigms
have recently attracted much research interest, such as weakly-/semi-supervised
learning, generative adversarial learning, etc. In this paper, we propose to
formulate annotation-efficient nucleus instance segmentation from the
perspective of few-shot learning (FSL). Our work was motivated by that, with
the prosperity of computational pathology, an increasing number of
fully-annotated datasets are publicly accessible, and we hope to leverage these
external datasets to assist nucleus instance segmentation on the target dataset
which only has very limited annotation. To achieve this goal, we adopt the
meta-learning based FSL paradigm, which however has to be tailored in two
substantial aspects before adapting to our task. First, since the novel classes
may be inconsistent with those of the external dataset, we extend the basic
definition of few-shot instance segmentation (FSIS) to generalized few-shot
instance segmentation (GFSIS). Second, to cope with the intrinsic challenges of
nucleus segmentation, including touching between adjacent cells, cellular
heterogeneity, etc., we further introduce a structural guidance mechanism into
the GFSIS network, finally leading to a unified Structurally-Guided Generalized
Few-Shot Instance Segmentation (SGFSIS) framework. Extensive experiments on a
couple of publicly accessible datasets demonstrate that, SGFSIS can outperform
other annotation-efficient learning baselines, including semi-supervised
learning, simple transfer learning, etc., with comparable performance to fully
supervised learning with less than 5% annotations.

在这篇论文中，我们提出了一种基于元学习的结构引导广义少样本实例分割（SGFSIS）框架，用于有效标注细胞核实例分割。实验证明，SGFSIS 在少于 5％的标注数据情况下，优于其他注释效率学习方法，包括半监督学习和简单迁移学习，并且与完全监督学习相当。