Conditional GANs based system for fibrosis detection and quantification in Hematoxylin and Eosin whole slide images

计算机科学 H&E染色 人工智能 数字化病理学 污渍 三色 分割 管道(软件) 病理 马森三色染色 深度学习 纤维化 模式识别(心理学) 计算机视觉 医学 染色 程序设计语言
作者
Ahmed M. Naglah,Fahmi Khalifa,Ayman El‐Baz,Dibson Gondim
出处
期刊:Medical Image Analysis [Elsevier BV]
卷期号:81: 102537-102537 被引量:11
标识
DOI:10.1016/j.media.2022.102537
摘要

Assessing the degree of liver fibrosis is fundamental for the management of patients with chronic liver disease, in liver transplants procedures, and in general liver disease research. The fibrosis stage is best assessed by histopathologic evaluation, and Masson's Trichrome stain (MT) is the stain of choice for this task in many laboratories around the world. However, the most used stain in histopathology is Hematoxylin Eosin (HE) which is cheaper, has a faster turn-around time and is the primary stain routinely used for evaluation of liver specimens. In this paper, we propose a novel digital pathology system that accurately detects and quantifies the footprint of fibrous tissue in HE whole slide images (WSI). The proposed system produces virtual MT images from HE using a deep learning model that learns deep texture patterns associated with collagen fibers. The training pipeline is based on conditional generative adversarial networks (cGAN), which can achieve accurate pixel-level transformation. Our comprehensive training pipeline features an automatic WSI registration algorithm, which qualifies the HE/MT training slides for the cGAN model. Using liver specimens collected during liver transplantation procedures, we conducted a range of experiments to evaluate the detected footprint of selected anatomical features. Our evaluation includes both image similarity and semantic segmentation metrics. The proposed system achieved enhanced results in the experiments with significant improvement over the state-of-the-art CycleGAN learning style, and over direct prediction of fibrosis in HE without having the virtual MT step.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
jia完成签到 ,获得积分10
刚刚
3秒前
3秒前
4秒前
Ava应助坚定路人采纳,获得10
6秒前
8秒前
9秒前
潘善若发布了新的文献求助10
9秒前
戴岱发布了新的文献求助10
11秒前
12秒前
13秒前
momo发布了新的文献求助10
15秒前
大模型应助潘善若采纳,获得10
15秒前
16秒前
jolt发布了新的文献求助10
17秒前
18秒前
传奇3应助戴岱采纳,获得10
19秒前
nini完成签到,获得积分10
21秒前
zzzjh发布了新的文献求助10
21秒前
量子星尘发布了新的文献求助10
22秒前
22秒前
24秒前
25秒前
nini发布了新的文献求助10
28秒前
Lucas应助XAN采纳,获得10
29秒前
潘善若发布了新的文献求助10
29秒前
棠棠完成签到 ,获得积分10
29秒前
32秒前
yyer完成签到,获得积分10
32秒前
34秒前
FashionBoy应助潘善若采纳,获得10
35秒前
慕青应助忐忑的阑香采纳,获得10
35秒前
momo发布了新的文献求助10
36秒前
冰淇淋完成签到,获得积分10
36秒前
XylonYu完成签到,获得积分10
37秒前
39秒前
坚定路人发布了新的文献求助10
41秒前
张宁波完成签到,获得积分0
42秒前
sue完成签到,获得积分10
43秒前
44秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989297
求助须知:如何正确求助?哪些是违规求助? 3531418
关于积分的说明 11253893
捐赠科研通 3270097
什么是DOI,文献DOI怎么找? 1804884
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809158