Polyp segmentation for colonoscopy images is of vital importance in clinical
practice. It can provide valuable information for colorectal cancer diagnosis
and surgery. While existing methods have achieved relatively good performance,
polyp segmentation still faces the following challenges: (1) Varying lighting
conditions in colonoscopy and differences in polyp locations, sizes, and
morphologies. (2) The indistinct boundary between polyps and surrounding
tissue. To address these challenges, we propose a Multi-scale information
sharing and selection network (MISNet) for polyp segmentation task. We design a
Selectively Shared Fusion Module (SSFM) to enforce information sharing and
active selection between low-level and high-level features, thereby enhancing
model's ability to capture comprehensive information. We then design a Parallel
Attention Module (PAM) to enhance model's attention to boundaries, and a
Balancing Weight Module (BWM) to facilitate the continuous refinement of
boundary segmentation in the bottom-up process. Experiments on five polyp
segmentation datasets demonstrate that MISNet successfully improved the
accuracy and clarity of segmentation result, outperforming state-of-the-art
methods.

通过多尺度信息共享和选择网络 (MISNet) 解决结直肠镜图像中息肉分割的问题，提高分割结果的准确性和清晰度。

多尺度信息共享和选择网络结合边界注意力用于息肉分割

Multi-scale Information Sharing and Selection Network with Boundary  Attention for Polyp Segmentation

Topological consistency plays a crucial role in the task of boundary
segmentation for reticular images, such as cell membrane segmentation in neuron
electron microscopic images, grain boundary segmentation in material
microscopic images and road segmentation in aerial images. In these fields,
topological changes in segmentation results have a serious impact on the
downstream tasks, which can even exceed the misalignment of the boundary
itself. To enhance the topology accuracy in segmentation results, we propose
the Skea-Topo Aware loss, which is a novel loss function that takes into
account the shape of each object and topological significance of the pixels. It
consists of two components. First, the skeleton-aware weighted loss improves
the segmentation accuracy by better modeling the object geometry with
skeletons. Second, a boundary rectified term effectively identifies and
emphasizes topological critical pixels in the prediction errors using both
foreground and background skeletons in the ground truth and predictions.
Experiments prove that our method improves topological consistency by up to 7
points in VI compared to 13 state-of-art methods, based on objective and
subjective assessments across three different boundary segmentation datasets.
The code is available at this https URL

为了提高分割结果的拓扑准确性，本文提出了一种新的损失函数 Skea-Topo Aware Loss，它考虑了每个对象的形状和像素的拓扑重要性，包括基于骨架的加权损失和基于边界修正的拓扑关键像素识别，实验证明我们的方法在三个不同的边界分割数据集上相比 13 种最先进的方法，通过客观和主观评估，提高了最多 7 个 VI 点的拓扑一致性。

基于骨架方法提高边界分割的拓扑准确性

Enhancing Boundary Segmentation for Topological Accuracy with  Skeleton-based Methods

In medical imaging, accurate image segmentation is crucial for quantifying
diseases, assessing prognosis, and evaluating treatment outcomes. However,
existing methods lack an in-depth integration of global and local features,
failing to pay special attention to abnormal regions and boundary details in
medical images. To this end, we present a novel deep learning-based approach,
MIPC-Net, for precise boundary segmentation in medical images. Our approach,
inspired by radiologists' working patterns, features two distinct modules: (i)
\textbf{Mutual Inclusion of Position and Channel Attention (MIPC) module}: To
enhance the precision of boundary segmentation in medical images, we introduce
the MIPC module, which enhances the focus on channel information when
extracting position features and vice versa; (ii) \textbf{GL-MIPC-Residue}: To
improve the restoration of medical images, we propose the GL-MIPC-Residue, a
global residual connection that enhances the integration of the encoder and
decoder by filtering out invalid information and restoring the most effective
information lost during the feature extraction process. We evaluate the
performance of the proposed model using metrics such as Dice coefficient (DSC)
and Hausdorff Distance (HD) on three publicly accessible datasets: Synapse,
ISIC2018-Task, and Segpc. Our ablation study shows that each module contributes
to improving the quality of segmentation results. Furthermore, with the
assistance of both modules, our approach outperforms state-of-the-art methods
across all metrics on the benchmark datasets, notably achieving a 2.23mm
reduction in HD on the Synapse dataset, strongly evidencing our model's
enhanced capability for precise image boundary segmentation. Codes will be
available at this https URL

基于深度学习的 MIPC-Net 方法用于精确医学图像边界分割，并在公开数据集上表现出优于现有方法的能力。

医学图像中的精确边界分割相互包含机制

A Mutual Inclusion Mechanism for Precise Boundary Segmentation in  Medical Images

Precise boundary segmentation of volumetric images is a critical task for
image-guided diagnosis and computer-assisted intervention, especially for
boundary confusion in clinical practice. However, U-shape networks cannot
effectively resolve this challenge due to the lack of boundary shape
constraints. Besides, existing methods of refining boundaries overemphasize the
slender structure, which results in the overfitting phenomenon due to networks'
limited abilities to model tiny objects. In this paper, we reconceptualize the
mechanism of boundary generation by encompassing the interaction dynamics with
adjacent regions. Moreover, we propose a unified network termed PnPNet to model
shape characteristics of the confused boundary region. Core ingredients of
PnPNet contain the pushing and pulling branches. Specifically, based on
diffusion theory, we devise the semantic difference module (SDM) from the
pushing branch to squeeze the boundary region. Explicit and implicit
differential information inside SDM significantly boost representation
abilities for inter-class boundaries. Additionally, motivated by the K-means
algorithm, the class clustering module (CCM) from the pulling branch is
introduced to stretch the intersected boundary region. Thus, pushing and
pulling branches will shrink and enlarge the boundary uncertainty respectively.
They furnish two adversarial forces to promote models to output a more precise
delineation of boundaries. We carry out experiments on three challenging public
datasets and one in-house dataset, containing three types of boundary confusion
in model predictions. Experimental results demonstrate the superiority of
PnPNet over other segmentation networks, especially on evaluation metrics of HD
and ASSD. Besides, pushing and pulling branches can serve as plug-and-play
modules to enhance classic U-shape baseline models. Codes are available.

通过包含与相邻区域的交互动态，重新构思边界生成机制，并提出了一种称为 PnPNet 的统一网络，用于建模混淆边界区域的形状特征。推拉分支提供了两种对抗性力量，以促使模型输出更精确的边界描绘。实验结果表明 PnPNet 在边界分割方面优于其他模型，并且推拉分支可以作为插拔式模块来增强经典的 U-shape 基准模型。