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 implemented with convolutional long-short term memory models. The registration network was trained in an unsupervised manner using pairs of planning CT (pCT) and CBCT images and produced a progressively deformed sequence of images. The segmentation network was optimized in a one-shot setting by combining progressively deformed pCT (anatomic context) and pCT delineations (shape context) with CBCT images. Our method, one-shot PACS was significantly more accurate (p$<$0.001) for tumor (DSC of 0.83 $\pm$ 0.08, surface DSC [sDSC] of 0.97 $\pm$ 0.06, and Hausdorff distance at $95^{th}$ percentile [HD95] of 3.97$\pm$3.02mm) and the esophagus (DSC of 0.78 $\pm$ 0.13, sDSC of 0.90$\pm$0.14, HD95 of 3.22$\pm$2.02) segmentation than multiple methods. Ablation tests and comparative experiments were also done.
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）
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. Thi…
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 …
Deepwise AI Lab（深睿医疗人工智能实验室）；School of Electronics Engineering and Computer Science, University of Peking（北京大学电子工程与计算机科学学院）；School of Automation, University of Northwestern Polytechnical（西北工业大学自动化学院）；Department of Computer Science, University of Hong Kong（香港大学计算机科学系）
Instance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN. The SAR-Net consists of three relation modules: 1. the anatomical structure relation module encoding spatial relations between diseases and anatomical parts. 2. the contextual relation module aggregating clues based on query-key pair of disease RoI and lung fields. 3. the disease relation modul…
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）
Stanford University（斯坦福大学）；Hokkaido University（北海道大学）；Global Institution for Collaborative Research and Education (GI-CoRE)（全球合作研究与教育机构（GI-CoRE））
This paper presents a dictionary learning-based method with region-specific image patches to maximize the utility of the powerful sparse data processing technique for CT image reconstruction. Considering heterogeneous distributions of image features and noise in CT, region-specific customization of dictionaries is utilized in iterative reconstruction. Thoracic CT images are partitioned into several regions according to their structural and noise characteristics. Dictionaries specific to each region are then learned from the segmented thoracic CT images and applied to subsequent image reconstru…
Body tissue composition is a long-known biomarker with high diagnostic and prognostic value in cardiovascular, oncological and orthopaedic diseases, but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. Therefore an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five di…
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis
Centre de recherche du CHU de Québec-Université Laval（魁北克大学拉瓦尔医院研究中心）；Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec-Université Laval（魁北克大学拉瓦尔心血管与呼吸病学研究所）
Purpose: CBCT-based adaptive radiotherapy requires daily images for accurate dose calculations. This study investigates the feasibility of applying a single convolutional network to facilitate CBCT-to-CT synthesis for head-and-neck, lung, and breast cancer patients. Methods: Ninety-nine patients diagnosed with head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based position verification were included in this study. CBCTs were registered to planning CTs according to clinical procedures. Three cycle-consistent generative adversarial networks (cycle-GANs) were trained in an u…
TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI
University Hospital Basel（巴塞尔大学医院）；University of Freiburg（弗莱堡大学）；University Hospital of Tuebingen（蒂宾根大学医院）；German Cancer Research Center（德国癌症研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy
LMU University Hospital, LMU Munich（慕尼黑大学附属医院，慕尼黑大学）；Radboud University Medical Center（拉德堡德大学医学中心）；University Medical Center Groningen, University of Groningen（格罗宁根大学医学中心，格罗宁根大学）；University Hospital of Cologne（科隆大学附属医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；German Cancer Consortium (DKTK)（德国癌症联盟）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；Skåne University Hospital（斯科讷大学医院）；Lund University（隆德大学）；Deutsches Krebsforschungszentrum (DKFZ)（德国癌症研究中心）
Generating Synthetic Contrast-Enhanced Chest CT Images from Non-Contrast Scans Using Slice-Consistent Brownian Bridge Diffusion Network
Department of Medical Imaging, University of Saskatchewan（医学成像系，萨斯喀彻温大学）；Department of Electrical and Computer Engineering, University of Saskatchewan（电气与计算机工程系，萨斯喀彻温大学）
TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
Department of Imaging, Radboudumc, Nijmegen, the Netherlands（影像部，Radboudumc，尼姆egen，荷兰）；Institute for Medical Informatics, University of Lübeck, Lübeck, Germany（医学信息研究所，吕贝克大学，吕贝克，德国）；Department of Nuclear Medicine, Hospital 108, Hanoi, Vietnam（核医学部，108医院，河内，越南）；Department of Diagnostic Imaging and Interventional Radiology, Thai Nguyen National Hospital, Thai Nguyen, Vietnam（诊断影像和介入放射科，Thai Nguyen国家医院，Thai Nguyen，越南）；Interventional Radiology Center, Tam Anh Hospital, Hanoi, Vietnam（介入放射科中心，Tam Anh医院，河内，越南）；FET, Vietnam National University, University of Engineering and Technology, Hanoi, Vietnam（FET，越南国家大学，工程与技术大学，河内，越南）
University of Science and Technology of China（中国科学技术大学）；Guangzhou First People’s Hospital（广州市第一人民医院）；South China University of Technology（华南理工大学）
In the paper, we present an approach for learning a single model that universally segments 33 anatomical structures, including vertebrae, pelvic bones, and abdominal organs. Our model building has to address the following challenges. Firstly, while it is ideal to learn such a model from a large-scale, fully-annotated dataset, it is practically hard to curate such a dataset. Thus, we resort to learn from a union of multiple datasets, with each dataset containing the images that are partially labeled. Secondly, along the line of partial labelling, we contribute an open-source, large-scale verteb…
Unidad Mixta de Imagen Biomédica FISABIO-CIPF（FISABIO-CIPF生物医学成像联合单元）；Universidad de Alicante（阿利坎特大学）；Medbravo；Hospital San Juan de Alicante（阿利坎特圣胡安医院）；Universidad Miguel Hernández（米格尔·埃尔南德斯大学）；Sierra Research SL（Sierra Research有限公司）；Bioinformatics & Biostatistics Unit, Principe Felipe Research Center（费利佩王子研究中心生物信息学与生物统计学单元）
This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests. The findings have been mapped onto standard Unified Medical Language System (UMLS) terminology and cover a wide spectrum of thoracic entities, unlike the considerab…
