基于特征金字塔和 Mask R-CNN 框架的泛癌图像分割。

Pan-cancer image segmentation based on feature pyramids and Mask R-CNN framework.

机构信息

School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai, China.

National Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology Chinese Academy of Science, Shanghai, China.

出版信息

Med Phys. 2024 Aug;51(8):5427-5440. doi: 10.1002/mp.17014. Epub 2024 Mar 4.

DOI:10.1002/mp.17014

PMID:38436455

Abstract

BACKGROUND

Cancer, a disease with a high mortality rate, poses a great threat to patients' physical and mental health and can lead to huge medical costs and emotional damage. With the continuous development of artificial intelligence technologies, deep learning-based cancer image segmentation techniques are becoming increasingly important in cancer detection and accurate diagnosis. However, in segmentation tasks, there are differences in efficiency between large and small objects and limited segmentation effects on objects of individual sizes. The previous segmentation frameworks still have room for improvement in multi-scale collaboration when segmenting objects.

PURPOSE

This paper proposes a method to train a deep learning segmentation framework using a feature pyramid processing dataset to improve the average precision (AP) index, and realizes multi-scale cooperation in target segmentation.

OBJECTIVE

Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification (PanNuke) dataset was selected to include approximately 7500 pathology images with cells from 19 different types of tissues, including five classifications of cancer, non-cancer, inflammation, death, and connective tissue.

METHODS

First, the method uses whole-slide images in the pan-cancer histology dataset for nuclei instance segmentation and classification (PanNuke) dataset, combined with the mask region convolutional neural network (Mask R-CNN) segmentation framework and improved loss function to segment and detect each cellular tissue in cancerous sections. Second, to address the problem of non-synergistic object segmentation at different scales in cancerous tissue segmentation, a scheme using feature pyramids to process the dataset was adopted as part of the feature extraction module.

RESULTS

Extensive experimental results on this dataset show that the method in this paper yields 0.269 AP and a boost of about 4% compared to the original Mask R-CNN framework.

CONCLUSIONS

It is effective and feasible to use feature pyramid to process data set to improve the effect of medical image segmentation.

摘要

背景

癌症是一种死亡率较高的疾病，对患者的身心健康构成极大威胁，并可能导致巨大的医疗费用和情感伤害。随着人工智能技术的不断发展，基于深度学习的癌症图像分割技术在癌症检测和准确诊断中变得越来越重要。然而，在分割任务中，大小物体的效率存在差异，对个别大小物体的分割效果有限。以前的分割框架在分割物体时在多尺度协作方面仍有改进的空间。

目的

本文提出了一种使用特征金字塔处理数据集训练深度学习分割框架的方法，以提高平均精度（AP）指标，并实现目标分割的多尺度合作。

目标

选择 Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification（PanNuke）数据集，其中包含来自 19 种不同组织类型的细胞的大约 7500 张病理图像，包括五种癌症、非癌症、炎症、死亡和结缔组织的分类。

方法

首先，该方法使用全幻灯片图像在 pan-cancer 组织学数据集用于核实例分割和分类（PanNuke）数据集，结合掩模区域卷积神经网络（Mask R-CNN）分割框架和改进的损失函数，分割和检测癌症切片中的每个细胞组织。其次，为了解决癌症组织分割中不同尺度上非协同目标分割的问题，采用了一种使用特征金字塔处理数据集的方案，作为特征提取模块的一部分。