DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search
PAII Inc.（PAII公司）；The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Ping An Insurance Company of China（中国平安保险（集团）股份有限公司）；Zhejiang Provincial People’s Hospital（浙江省人民医院）
Dazhou Guo, Xianghua Ye, Jia Ge, Xing Di, Le Lu, Lingyun Huang, Guotong Xie, Jing Xiao, Zhongjie Liu, Ling Peng, Senxiang Yan, Dakai Jin
Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow. High inter-user variabilities across oncologists and prohibitive laboring costs motivated the automated approach. Previous works exploit anatomical priors to infer LNS based on predefined ad-hoc margins. However, without voxel-level supervision, the performance is severely limited. LNS is highly context-dependent - LNS boundaries are constrained by anatomical organs - we formulate it as a deep spatial and contextual parsing problem via encoded anatomical organs. This permits the deep network to better learn from both CT appearance and organ context. We develop a stratified referencing organ segmentation protocol that divides the organs into anchor and non-anchor categories and uses the former's predictions to guide the later segmentation. We further develop an auto-search module to identify the key organs that opt for the optimal LNS parsing performance. Extensive four-fold cross-validation experiments on a dataset of 98 esophageal cancer patients (with the most comprehensive set of 12 LNSs + 22 organs in thoracic region to date) are conducted. Our LNS parsing model produces significant performance improvements, with an average Dice score of 81.1% +/- 6.1%, which is 5.0% and 19.2% higher over the pure CT-based deep model and the previous representative approach, respectively.
Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer. Accurate segmentation of organs surrounding tumours helps account for the variation in position and morphology inherent across patients, thereby facilitating adaptive and computer-assisted radiotherapy. Although manual delineation of OARs is still highly prevalent, it is prone to errors due to complex variations in the shape and position of organs across patients, and low soft tissue contrast between neighbouring organs…
Department of Science and Technology, Linkoping University（林雪平大学科技学院）；Sectra AB（Sectra公司）；Center for Medical Image Science and Visualization, Linkoping University（林雪平大学医学影像科学与可视化中心）
The scarcity of labeled data is a major bottleneck for developing accurate and robust deep learning-based models for histopathology applications. The problem is notably prominent for the task of metastasis detection in lymph nodes, due to the tissue's low tumor-to-non-tumor ratio, resulting in labor- and time-intensive annotation processes for the pathologists. This work explores alternatives on how to augment the training data for colon carcinoma metastasis detection when there is limited or no representation of the target domain. Through an exhaustive study of cross-validated experiments wit…
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Georgia Institute of Technology（佐治亚理工学院）
Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identifying spatial regions responsible for significant contributions to the model's prediction. In contrast, expert radiologists first locate the prominent anatomical structures before determining if those regions are anomalous. Therefore, integrating anatomical knowledge within deep learning models could bring substantial improvement in automatic disease classification. Motivated by …
University of Bedfordshire（贝德福特大学）；Perm State University（彼尔姆国立大学）；Schaeffler Group（舍弗勒集团）；Universiti Brunei Darussalam（文莱达鲁萨兰大学）；Abramov Software GmbH & Co. KG（阿布拉莫夫软件两合有限公司）
Pathologists find tedious to examine the status of the sentinel lymph node on a large number of pathological scans. The examination process of such lymph node which encompasses metastasized cancer cells is histopathologically organized. However, the task of finding metastatic tissues is gradual which is often challenging. In this work, we present our deep convolutional neural network based model validated on PatchCamelyon (PCam) benchmark dataset for fundamental machine learning research in histopathology diagnosis. We find that our proposed model trained with a semi-supervised learning approa…
Memorial Sloan-Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）
We implemented and evaluated a multiple resolution residual network (MRRN) for multiple normal organs-at-risk (OAR) segmentation from computed tomography (CT) images for thoracic radiotherapy treatment (RT) planning. Our approach simultaneously combines feature streams computed at multiple image resolutions and feature levels through residual connections. The feature streams at each level are updated as the images are passed through various feature levels. We trained our approach using 206 thoracic CT scans of lung cancer patients with 35 scans held out for validation to segment the left and r…
INSA Rouen（鲁昂国立应用科学学院）；Normandie Univ（诺曼底大学）；UNIROUEN（鲁昂大学）；UNIHAVRE（勒阿弗尔大学）；Centre Henri Becquerel（亨利·贝克勒尔中心）
In the era of open science, public datasets, along with common experimental protocol, help in the process of designing and validating data science algorithms; they also contribute to ease reproductibility and fair comparison between methods. Many datasets for image segmentation are available, each presenting its own challenges; however just a very few exist for radiotherapy planning. This paper is the presentation of a new dataset dedicated to the segmentation of organs at risk (OARs) in the thorax, i.e. the organs surrounding the tumour that must be preserved from irradiations during radiothe…
LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Yunu, Inc.（尤努公司）；Isomics Inc（伊索米克斯公司）；Dana-Farber Cancer Institute（丹娜法伯癌症研究院）；Technical University Munich（慕尼黑工业大学）；Helmholtz Munich（慕尼黑亥姆霍兹中心）；Munich Center of Machine Learning (MCML)（慕尼黑机器学习中心）；King’s College London（伦敦国王学院）；University of Lübeck（吕贝克大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；Niigata University of Health and Welfare（新潟医疗福祉大学）；Muroran Institute of Technology（室兰工业大学）；Shanghai Jiao Tong University（上海交通大学）；University of Electronic Science and Technology of China（电子科技大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；University of Heidelberg（海德堡大学）；Shanghai AI laboratory（上海人工智能实验室）
University of New South Wales（新南威尔士大学）；Prince of Wales Hospital（威尔士亲王医院）
Computer aided diagnostics often requires analysis of a region of interest (ROI) within a radiology scan, and the ROI may be an organ or a suborgan. Although deep learning algorithms have the ability to outperform other methods, they rely on the availability of a large amount of annotated data. Motivated by the need to address this limitation, an approach to localisation and detection of multiple organs based on supervised and semi-supervised learning is presented here. It draws upon previous work by the authors on localising the thoracic and lumbar spine region in CT images. The method genera…
PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Ping An Technology（平安科技）
Clinical target volume (CTV) delineation from radiotherapy computed tomography (RTCT) images is used to define the treatment areas containing the gross tumor volume (GTV) and/or sub-clinical malignant disease for radiotherapy (RT). High intra- and inter-user variability makes this a particularly difficult task for esophageal cancer. This motivates automated solutions, which is the aim of our work. Because CTV delineation is highly context-dependent--it must encompass the GTV and regional lymph nodes (LNs) while also avoiding excessive exposure to the organs at risk (OARs)--we formulate it as a…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Image-guided adaptive lung radiotherapy requires accurate tumor and organs segmentation from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs are hard to segment because of low soft-tissue contrast, imaging artifacts, respiratory motion, and large treatment induced intra-thoracic anatomic changes. Hence, we developed a novel Patient-specific Anatomic Context and Shape prior or PACS-aware 3D recurrent registration-segmentation network for longitudinal thoracic CBCT segmentation. Segmentation and registration networks were concurrently trained in an end-to-end framework and implemente…
Emory University（埃默里大学）；Winship Cancer Institute（温希普癌症研究所）
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications. These methods were classified into six categories according to their network design. For each category, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review of each category, we briefly discussed its achievements, shortcomings and future potentials. We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorga…
University of Warwick（华威大学）；Tissue Image Analytics (TIA) Centre, University of Warwick（华威大学组织图像分析中心）
The problem of recognizing various types of tissues present in multi-gigapixel histology images is an important fundamental pre-requisite for downstream analysis of the tumor microenvironment in a bottom-up analysis paradigm for computational pathology. In this paper, we propose a deep dictionary learning approach to solve the problem of tissue phenotyping in histology images. We propose deep Multi-Resolution Dictionary Learning (deepMRDL) in order to benefit from deep texture descriptors at multiple different spatial resolutions. We show the efficacy of the proposed approach through extensive…
As lung cancer evolves, the presence of enlarged and potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strategy. Following the clinical guidelines, estimation of short-axis diameter and mediastinum station are paramount for correct diagnosis. A method for accurate and automatic segmentation is hence decisive for quantitatively describing lymph nodes. In this study, the use of 3D convolutional neural networks, either through slab-wise schemes or the leveraging of downsampled entire volumes, is investigated. Furthermore, the…
Ping An Technology（平安科技）；Changhai Hospital（长海医院）；PAII Inc.（PAII公司）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Tianjin Medical University Cancer Institute and Hospital（天津医科大学肿瘤医院）
Lymph node (LN) metastasis status is one of the most critical prognostic and cancer staging factors for patients with resectable pancreatic ductal adenocarcinoma (PDAC), or in general, for any types of solid malignant tumors. Preoperative prediction of LN metastasis from non-invasive CT imaging is highly desired, as it might be straightforwardly used to guide the following neoadjuvant treatment decision and surgical planning. Most studies only capture the tumor characteristics in CT imaging to implicitly infer LN metastasis and very few work exploit direct LN's CT imaging information. To the b…
EXACT: an explainable anomaly-aware vision foundation model for analysis of 3D chest CT
School of Biomedical Engineering, Tsinghua University（清华大学生物医学工程学院）；Department of Radiology, Mianyang Central Hospital（绵阳市中心医院放射科）；Center for Biomedical Imaging Research, Tsinghua University（清华大学生物医学成像研究中心）
Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis
School of Electrical Engineering, Korea University, Seoul 02841, Korea（韩国大学电子工程学院）；Department of Pathology, Korea University Anam Hospital and Department of Biomedical Informatics, Korea University College of Medicine, Seoul 02841, Korea（韩国大学医学院病理学系）；Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea（韩国天主大学医学院圣玛丽医院医院病理学系）
A Computational Pipeline for Patient-Specific Modeling of Thoracic Aortic Aneurysm: From Medical Image to Finite Element Analysis
Department of Computer Science, University of Miami（迈阿密大学计算机科学系）；Sutra Medical Inc（Sutra医疗公司）；Aortic Institute at Yale-New Haven Hospital, Yale University School of Medicine（耶鲁-纽黑文医院主动脉研究所，耶鲁大学医学院）
Digitization of histopathology slides has led to several advances, from easy data sharing and collaborations to the development of digital diagnostic tools. Deep learning (DL) methods for classification and detection have shown great potential, but often require large amounts of training data that are hard to collect, and annotate. For many cancer types, the scarceness of data creates barriers for training DL models. One such scenario relates to detecting tumor metastasis in lymph node tissue, where the low ratio of tumor to non-tumor cells makes the diagnostic task hard and time-consuming. DL…
SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电子工程学院）；Department of Radiation Oncology, Sichuan Cancer Center, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, University of Electronic Science and Technology of China（四川省肿瘤医院放射肿瘤科）；Shanghai AI Lab, Shanghai, China（上海人工智能实验室）；The Affiliated Hospital of Qingdao University, Qingdao, China（青岛大学附属医院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China（香港中文大学计算机科学与工程系）；Faculty of Information Technology, Monash University, Melbourne, Australia（墨尔本大学信息科技学院）；Department of Computing and Department of Brain Sciences, Imperial College London, United Kingdom（伦敦帝国理工学院计算机系和脑科学系）；School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China（西北工业大学计算机科学与工程学院）；UMIT Tirol - Private University for Health Sciences and Health Technology, Austria（蒂罗尔大学（私人健康科学与健康技术大学））；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；National Heart and Lung Institute, Imperial College London, London, United Kingdom（伦敦帝国理工学院国家心脏和肺研究所）；Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom（伦敦大学学院霍克斯研究所）；Shanghai MediWorks Precision Instruments Co., Ltd., China（上海 MediWorks 精密仪器有限公司）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China（上海交通大学自动化与智能感知学院）；Department of Systems Hub, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China（香港科技大学（广州）系统中心）
Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Department of Pharmacy and Biotechnology, University of Bologna（博洛尼亚大学药学与生物技术系）；Center for Biomolecular Nanotechnologies, Istituto Italiano di Tecnologia（意大利技术研究院生物分子纳米技术中心）；Faculty of Mathematics and Computer Science, Jagiellonian University（杰齐兰大学数学与计算机科学学院）；Department of Cardiology and Internal Medicine, University of Warmia and Mazury（马尔堡大学心内科与内科系）；Diagnostic and Treatment Center Gammed（Gammed诊断与治疗中心）；Department of Bioengineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校生物工程系）；Department of General Surgery, Peking University Third Hospital（北京大学第三医院普外科）；Department of Radiology & Biomedical Imaging, University of California, San Francisco（加州大学旧金山分校放射科与生物医学成像系）；Department of Bioengineering, University of California, Berkeley（加州大学伯克利分校生物工程系）；Department of Radiation Oncology, Johns Hopkins School of Medicine（约翰霍普金斯医学院放射肿瘤学系）；NVIDIA Code, Models & Data（NVIDIA代码、模型与数据）
