This paper explores the use of a soft ground-truth mask ("soft mask'') to train a Fully Convolutional Neural Network (FCNN) for segmentation of Multiple Sclerosis (MS) lesions. Detection and segmentation of MS lesions is a complex task largely due to the extreme unbalanced data, with very small number of lesion pixels that can be used for training. Utilizing the anatomical knowledge that the lesion surrounding pixels may also include some lesion level information, we suggest to increase the data set of the lesion class with neighboring pixel data - with a reduced confidence weight. A soft mask is constructed by morphological dilation of the binary segmentation mask provided by a given expert, where expert-marked voxels receive label 1 and voxels of the dilated region are assigned a soft label. In the methodology proposed, the FCNN is trained using the soft mask. On the ISBI 2015 challenge dataset, this is shown to provide a better precision-recall tradeoff and to achieve a higher average Dice similarity coefficient. We also show that by using this soft mask scheme we can improve the network segmentation performance when compared to a second independent expert.
PAII Inc（PAII公司）；Ping An Technology（平安科技）；Chang Gung Memorial Hospital（长庚纪念医院）
Monitoring treatment response in longitudinal studies plays an important role in clinical practice. Accurately identifying lesions across serial imaging follow-up is the core to the monitoring procedure. Typically this incorporates both image and anatomical considerations. However, matching lesions manually is labor-intensive and time-consuming. In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals. To incorporate anatomical constraints, we propose an anatomical signal encoder, which prevents lesions being matched w…
SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation
Imperial College London（帝国理工学院）；Data Science Institute, Imperial College London（帝国理工学院数据科学研究所）；UK Dementia Research Institute, Imperial College London（帝国理工学院英国痴呆症研究所）；Rosalind Franklin Institute（罗莎琳德·富兰克林研究所）
SD-RetinaNet: Topologically Constrained Semi-Supervised Retinal Lesion and Layer Segmentation in OCT
Christian Doppler Laboratory for Artificial Intelligence in Retina（基督教多普勒人工智能视网膜实验室）；Institute of Artificial Intelligence（人工智能研究所）；Center for Medical Data Science（医学数据科学中心）；Medical University of Vienna（维也纳医学大学）；Computational Imaging Research Lab（计算成像研究实验室）；Department of Biomedical Imaging and Image-Guided Therapy（生物医学成像与影像引导治疗部门）；Department of Ophthalmology and Optometry（眼科与视光学部门）
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland（瑞士西部高等专业学院瓦莱州瓦利斯分校）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；ALTA Institute, University of Cambridge（剑桥大学ALTA研究所）；Yandex Research（Yandex研究院）；Isomorphic Labs（同构实验室）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；University Hospital Basel and University of Basel（巴塞尔大学医院与巴塞尔大学）
Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹信息与健康数据科学学院（HIDSS4Health））；Institute for AI in Medicine (IKIM), University Hospital Essen（埃森大学医院医学人工智能研究所（IKIM））
Multiple Myeloma Lesion Segmentation on Whole-Body Diffusion-Weighted Imaging via Efficient Anatomical Anticipation and Multimodal Confirmation
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统全国重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院放射科）
SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints
Imperial College London（伦敦帝国学院）；Data Science Institute, Imperial College London（伦敦帝国学院数据科学研究所）；Beijing Institute of Technology（北京理工大学）；UK Dementia Research Institute（英国痴呆症研究所）；Rosalind Franklin Institute（罗莎琳德·富兰克林研究所）
Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation
Leiden University（莱顿大学）；Leiden University Medical Center（莱顿大学医学中心）；Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学医学中心（阿姆斯特丹大学））；University Medical Center Utrecht（乌得勒支大学医学中心）；University Hospitals Leuven（鲁汶大学医院）；KU Leuven（鲁汶大学）；Van’t Hoff Institute for Molecular Sciences, University of Amsterdam（阿姆斯特丹大学范特霍夫分子科学研究所）
Southern University of Science and Technology（南方科技大学）；The Chinese University of Hong Kong（香港中文大学）；Air Force Medical Center, PLA（中国人民解放军空军特色医学中心）
Colonoscopy is a standard imaging tool for visualizing the entire gastrointestinal (GI) tract of patients to capture lesion areas. However, it takes the clinicians excessive time to review a large number of images extracted from colonoscopy videos. Thus, automatic detection of biological anatomical landmarks within the colon is highly demanded, which can help reduce the burden of clinicians by providing guidance information for the locations of lesion areas. In this article, we propose a novel deep learning-based approach to detect biological anatomical landmarks in colonoscopy videos. First, …
CAFusion: Controllable Anatomical Synthesis of Perirectal Lymph Nodes via SDF-guided Diffusion
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；Anhui Medical University（安徽医科大学）
Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT
Institute for AI in Medicine (IKIM)（医学人工智能研究所）；University Hospital Essen (AöR)（埃森大学医院）；Department of Nuclear Medicine（核医学科）；Cancer Research Center Cologne Essen (CCCE)（科隆埃森癌症研究中心）；West German Cancer Center Essen University Hospital Essen (AöR)（埃森大学医院西德癌症中心）；TU Dortmund（多特蒙德工业大学）；German Cancer Consortium (DKTK, Partner site Essen)（德国癌症联盟（埃森合作站点））
本研究针对 PET/CT 病变分割，提出基于示踪剂分类和集成 nnUNet 的方法，融合解剖标签，在 autoPET III 挑战中取得高 Dice 分数。
University of Utah（犹他大学）；Sorbonne University（索邦大学）；Inria（法国国家信息与自动化研究所）；Florida International University（佛罗里达国际大学）；Inserm（法国国家健康与医学研究院）；CNRS（法国国家科学研究中心）
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications (e.g., implant design and lesion screening). Here, we systematically assess the out…
Inselspital, Bern University Hospital（伯尔尼大学医院（Inselspital））；Spital Tiefenau（Tiefenau医院）；Medizinisch Radiologisches Institut（医学放射学研究所）；University of Bern（伯尔尼大学）
Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters:…
Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings. In this study we address the case where the experts' opinion is obtained as a distribution over the possible values. We propose a soft version of the STAPLE algorithm for experts' markings fusion that can handle soft values. The algorithm was applied to obtain consensus from soft Multiple Sclerosis (MS) segmentation masks. Soft MS segmentations are constructed from manual binary delineations by including lesion surrounding voxels in the segmentation mask with a r…
Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion delineation. In this paper, we propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. By controlling feature information passing through two complementary directions,…
Generalizable automated ischaemic stroke lesion segmentation with vision transformers
University College London（伦敦大学学院）；UCL Queen Square Institute of Neurology（伦敦大学学院女王广场神经病学研究所）；National Hospital for Neurology and Neurosurgery（国家神经病学与神经外科医院）；King’s College London（伦敦国王学院）；Institut des Maladies Neurodégénératives（神经退行性疾病研究所）；CNRS（法国国家科学研究中心）；CEA（法国原子能和替代能源委员会）；University of Bordeaux（波尔多大学）
Nanjing University of Information Science & Technology（南京信息工程大学）
Cerebrovascular accident, or commonly known as stroke, is an acute disease with extreme impact on patients and healthcare systems and is the second largest cause of death worldwide. Fast and precise stroke lesion detection and location is an extreme important process with regards to stroke diagnosis, treatment, and prognosis. Except from the manual segmentation approach, machine learning based segmentation methods are the most promising ones when considering efficiency and accuracy, and convolutional neural network based models are the first of its kind. However, most of these neural network m…
Automatic segmentation of tumor lesions is a critical initial processing step for quantitative PET/CT analysis. However, numerous tumor lesions with different shapes, sizes, and uptake intensity may be distributed in different anatomical contexts throughout the body, and there is also significant uptake in healthy organs. Therefore, building a systemic PET/CT tumor lesion segmentation model is a challenging task. In this paper, we propose a novel step-by-step 3D segmentation method to address this problem. We achieved Dice score of 0.92, false positive volume of 0.89 and false negative volume …
We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets, require different loss functions for optimal performance due to inherent differences in their clinical objectives. We investigate distribution, region and boundary-based loss functions for both tasks across 200 patient exams from the publicly-available ProstateX dataset. For evaluation, we conduct a thorough comparative analysis of model predictions and calibration, measured with respect to multi-class volume segmentat…
RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning
Division of Hematology, The Ohio State University Comprehensive Cancer Center（血液科，俄亥俄州立大学综合癌症中心）；Department of Radiology, Mahidol University（医学放射科，玛希多大学）；Department of Radiology, Mettapracharak Hospital（医学放射科，Mettapracharak医院）；Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center（放射肿瘤科，俄亥俄州立大学综合癌症中心）
PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Baidu Inc.（百度公司）；Sun Yat-sen University（中山大学）；Agency for Science, Technology and Research（新加坡科技研究局）；CONICET（阿根廷国家科学与技术研究理事会）；UNICEN（阿根廷国立中央大学）；Medical University of Vienna（维也纳医科大学）
Synthetic training for long-tail haemorrhagic lesion segmentation in data-scarce settings
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡弗里德里希-亚历山大大学（FAU））；Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；German Centre for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心（DZNE））
Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment. We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. The p…
King’s College London（伦敦国王学院）；Shanghai Jiao Tong University（上海交通大学）
Accurate stroke lesion segmentation plays a pivotal role in stroke rehabilitation research, to provide lesion shape and size information which can be used for quantification of the extent of the stroke and to assess treatment efficacy. Recently, automatic segmentation algorithms using deep learning techniques have been developed and achieved promising results. In this report, we present our stroke lesion segmentation model based on nnU-Net framework, and apply it to the Anatomical Tracings of Lesions After Stroke (ATLAS v2.0) dataset. Furthermore, we describe an effective post-processing strat…
Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans
King Fahd University of Petroleum and Minerals（法赫德国王石油矿产大学）；Vinbrain JSC（Vinbrain股份公司）；Le Quy Don Technical University（黎贵敦技术大学）；University Medical Center at Ho Chi Minh City（胡志明市大学医疗中心）
Hamburg University of Technology（汉堡工业大学）；Forschungszentrum Medizintechnik Hamburg（汉堡医疗技术研究中心）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）
In this paper, we describe our method for the ISIC 2019 Skin Lesion Classification Challenge. The challenge comes with two tasks. For task 1, skin lesions have to be classified based on dermoscopic images. For task 2, dermoscopic images and additional patient meta data have to be used. A diverse dataset of 25000 images was provided for training, containing images from eight classes. The final test set contains an additional, unknown class. We address this challenging problem with a simple, data driven approach by including external data with skin lesions types that are not present in the train…
Automatic segmentation of tumor lesions is a critical initial processing step for quantitative PET/CT analysis. However, numerous tumor lesion with different shapes, sizes, and uptake intensity may be distributed in different anatomical contexts throughout the body, and there is also significant uptake in healthy organs. Therefore, building a systemic PET/CT tumor lesion segmentation model is a challenging task. In this paper, we propose a novel training strategy to build deep learning models capable of systemic tumor segmentation. Our method is validated on the training set of the AutoPET 202…
Align then Refine: Text-Guided 3D Prostate Lesion Segmentation
Department of Radiology, Northwestern University, Chicago, USA（放射科，西北大学，芝加哥，美国）；Department of Urology, Northwestern University, Chicago, USA（泌尿科，西北大学，芝加哥，美国）；Center for Cancer Research, National Cancer Institute, Bethesda, USA（癌症研究中心，国家癌症研究所，贝塞斯达，美国）
Distilling Expert Surgical Knowledge: How to train local surgical VLMs for anatomy explanation in Complete Mesocolic Excision
1 Institute of Medical Technology；Intelligent Systems, Hamburg University of Technology, Hamburg, Germany 2 Department of General, Visceral；Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
Ultrasound imaging is generally employed for real-time investigation of internal anatomy of the human body for disease identification. Delineation of the anatomical boundary of organs and pathological lesions is quite challenging due to the stochastic nature of speckle intensity in the images, which also introduces visual fatigue for the observer. This paper introduces a fully convolutional neural network based method to segment organ and pathologies in ultrasound volume by learning the spatial-relationship between closely related classes in the presence of stochastically varying speckle inten…
New Jersey Institute of Technology（新泽西理工学院）；Kessler Foundation（凯斯勒基金会）；Rutgers – New Jersey Medical School（罗格斯大学新泽西医学院）；Medical College of Wisconsin（威斯康星医学院）；Rutgers University – Newark（罗格斯大学纽瓦克分校）
Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. We propose a multi-modal multi-path convolutional neural network system for automating stroke lesion segmentation. Our system has nine end-to-end UNets that take as input 2-dimensional (2D) slices and examines all three planes with three different normalizations. Outputs from these nine total paths are concatenated into a 3D volume that is then passed to a 3D convolutional neural network to output a final lesion mask. We tr…
For several skin conditions such as vitiligo, accurate segmentation of lesions from skin images is the primary measure of disease progression and severity. Existing methods for vitiligo lesion segmentation require manual intervention. Unfortunately, manual segmentation is time and labor-intensive, as well as irreproducible between physicians. We introduce a convolutional neural network (CNN) that quickly and robustly performs vitiligo skin lesion segmentation. Our CNN has a U-Net architecture with a modified contracting path. We use the CNN to generate an initial segmentation of the lesion, th…
Tel Aviv University（特拉维夫大学）；Sheba Medical Center（示巴医疗中心）；Bar-Ilan University（巴伊兰大学）
In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset contains CT examinations from 140 patients with 333 CT images annotated by an expert radiologist. We tested our approach and compared it to the conventional training process. Results indicate superiority o…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
Diabetic retinopathy (DR) is the most common form of diabetic eye disease. Retinopathy can affect all diabetic patients and becomes particularly dangerous, increasing the risk of blindness, if it is left untreated. The success rate of its curability solemnly depends on diagnosis at an early stage. The development of automated computer aided disease diagnosis tools could help in faster detection of symptoms with a wider reach and reasonable cost. This paper proposes a method for the automated segmentation of retinal lesions and optic disk in fundus images using a deep fully convolutional neural…
Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Duke University（杜克大学）；Singapore General Hospital（新加坡中央医院）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）
Brain tissue segmentation from multimodal MRI is a key building block of many neuroscience analysis pipelines. It could also play an important role in many clinical imaging scenarios. Established tissue segmentation approaches have however not been developed to cope with large anatomical changes resulting from pathology. The effect of the presence of brain lesions, for example, on their performance is thus currently uncontrolled and practically unpredictable. Contrastingly, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly and is achieving …
Automatic and accurate lesion segmentation is critical for clinically estimating the lesion statuses of stroke diseases and developing appropriate diagnostic systems. Although existing methods have achieved remarkable results, further adoption of the models is hindered by: (1) inter-class indistinction, the normal brain tissue resembles the lesion in appearance. (2) intra-class inconsistency, large variability exists between different areas of the lesion. To solve these challenges in stroke segmentation, we propose a novel method, namely Attention Guided Multiscale Recovery framework (AGMR-Net…
