Automated segmentation can assist radiotherapy treatment planning by saving manual contouring efforts and reducing intra-observer and inter-observer variations. The recent development of deep learning approaches has revoluted medical data processing, including semantic segmentation, by dramatically improving performance. However, training effective deep learning models usually require a large amount of high-quality labeled data, which are often costly to collect. We developed a novel semi-supervised adversarial deep learning approach for 3D pelvic CT image semantic segmentation. Unlike supervised deep learning methods, the new approach can utilize both annotated and un-annotated data for training. It generates un-annotated synthetic data by a data augmentation scheme using generative adversarial networks (GANs). We applied the new approach to segmenting multiple organs in male pelvic CT images, where CT images without annotations and GAN-synthesized un-annotated images were used in semi-supervised learning. Experimental results, evaluated by three metrics (Dice similarity coefficient, average Hausdorff distance, and average surface Hausdorff distance), showed that the new method achieved either comparable performance with substantially fewer annotated images or better performance with the same amount of annotated data, outperforming the existing state-of-the-art methods.
Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pelvic CT image segmentation using deep learning. The architecture consists of a 2D localization network followed by a 3D segmentation network for volumetric segmentation of prostate, bladder, rectum, and femoral heads. We used a multi-channel 2D U-Net followed by a 3D U-Net with encoding arm modified with aggregated residual networks, known as ResNeXt. The models were trained and tested on a pelvic CT image dataset co…
Washington University（华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）
Purpose: The research is to develop a novel CNN-based adversarial deep learning method to improve and expedite the multi-organ semantic segmentation of CT images, and to generate accurate contours on pelvic CT images. Methods: Planning CT and structure datasets for 120 patients with intact prostate cancer were retrospectively selected and divided for 10-fold cross-validation. The proposed adversarial multi-residual multi-scale pooling Markov Random Field (MRF) enhanced network (ARPM-net) implements an adversarial training scheme. A segmentation network and a discriminator network were trained …
Pelvic floor MRI segmentation based on semi-supervised deep learning
Institute of Medical Technology, Peking University Health Science Center, Peking University（北京大学医学部医学技术研究院，北京大学）；Biomedical Engineering Department, Peking University（北京大学生物医学工程系）；University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University（上海交通大学密西根学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）；Department of Mechanical Engineering, University of Michigan（密歇根大学机械工程系）；Department of Obstetrics and Gynecology, University of Michigan（密歇根大学妇产科）
Peking University（北京大学）；Indiana University Bloomington（印第安纳大学伯明顿分校）；Peking Union Medical College Hospital（北京协和医院）
One of the time-consuming routine work for a radiologist is to discern anatomical structures from tomographic images. For assisting radiologists, this paper develops an automatic segmentation method for pelvic magnetic resonance (MR) images. The task has three major challenges 1) A pelvic organ can have various sizes and shapes depending on the axial image, which requires local contexts to segment correctly. 2) Different organs often have quite similar appearance in MR images, which requires global context to segment. 3) The number of available annotated images are very small to use the latest…
University of Oxford（牛津大学）；University College London（伦敦大学学院）；InstaDeep（InstaDeep公司）；City University of Hong Kong（香港城市大学）；Radboud University Nijmegen Medical Centre（拉德堡德大学奈梅亨医学中心）
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitations in deploying modern deep learning models to clinical practice, expertise-and-labour-intensive labelling and cross-institution generalisation. This work presents the first 3D few-shot interclass segmentation network for medical images, using a labelled multi-institution dataset from prostate canc…
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…
High-Fidelity 3D Geometric Reconstruction of Pelvic Organs from MRI: A Hybrid Deep Learning and Iterative Optimization Approach
Institute of Medical Technology, Peking University Health Science Center, Peking University（北京大学医学部医学技术研究院，北京大学）；Biomedical Engineering Department, Institute of Advanced Clinical Medicine, Peking University（北京大学先进临床医学研究院生物医学工程系）；Department of Obstetrics and Gynecology, Peking University People’s Hospital（北京大学人民医院妇产科部）
Microsoft Research（微软研究院）；Carnegie Mellon University（卡内基梅隆大学）；University of California, San Diego（加州大学圣地亚哥分校）；Imperial College London（帝国理工学院）
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus layers adaptively change the size of the effective receptive field based on the processed context to generate more powerful features. This is achieved by parallelising multiple convolutional layers with different dilation rates, combined by an attention mechanism that learns to focus on the optimal scales driven by context. By sharing the weights of the parallel convolutions we make the network scale-invariant, with onl…
Contrastive uncertainty learning for robust multi-structure segmentation in lower-pelvic MR
UCL Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）；Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学学院医学物理与生物医学工程系）；Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London（伦敦大学玛丽女王学院工程与材料科学学院生物工程中心）；Digital Environment Research Institute, Queen Mary University of London（伦敦大学玛丽女王学院数字环境研究所）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
University of Waterloo（滑铁卢大学）；Sunnybrook Health Sciences Centre（森尼布鲁克健康科学中心）
Marking tumors and organs is a challenging task suffering from both inter- and intra-observer variability. The literature quantifies observer variability by generating consensus among multiple experts when they mark the same image. Automatically building consensus contours to establish quality assurance for image segmentation is presently absent in the clinical practice. As the \emph{big data} becomes more and more available, techniques to access a large number of existing segments of multiple experts becomes possible. Fast algorithms are, hence, required to facilitate the search for similar c…
PDC-Net: Pattern Divide-and-Conquer Network for Pelvic Radiation Injury Segmentation
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University（中山大学第六附属医院放射科）；School of Computer and Information Engineering, Jiangxi Normal University（江西师范大学计算机与信息工程学院）；Department of General Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University（中山大学第六附属医院普外科）；Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University（广东省直肠及盆底疾病重点实验室，生物医学创新中心，中山大学第六附属医院）
Partial Decoder Attention Network with Contour-weighted Loss Function for Data-Imbalance Medical Image Segmentation
Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China（北京大学医学部医学技术研究所）；National Institute of Health Data Science, Peking University, Beijing, China（北京大学国家健康数据科学研究院）；Department of Radiology, Peking University Third Hospital, Beijing, China（北京大学第三医院放射科）；RIKEN Center for Computational Science (R-CCS), Kobe, Japan（日本京都大学RIKEN计算科学中心）；National Institute of Health Data Science, Peking University, the Institute of Medical Technology, Peking University Health Science Center, and the Institute for Artificial Intelligence, Peking University, Beijing, China（北京大学国家健康数据科学研究院、北京大学医学部医学技术研究所以及北京大学人工智能研究所）
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…
Medical Center University of Freiburg（弗莱堡大学医学中心）；Faculty of Medicine, University of Freiburg（弗莱堡大学医学院）；German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））；German Oncology Center, European University（欧洲大学德国肿瘤学中心）
Despite the success of convolutional neural networks for 3D medical-image segmentation, the architectures currently used are still not robust enough to the protocols of different scanners, and the variety of image properties they produce. Moreover, access to large-scale datasets with annotated regions of interest is scarce, and obtaining good results is thus difficult. To overcome these challenges, we introduce IB-U-Nets, a novel architecture with inductive bias, inspired by the visual processing in vertebrates. With the 3D U-Net as the base, we add two 3D residual components to the second enc…
Université de Bourgogne Franche-Comté（勃艮第-弗朗什孔泰大学）；CGFL（CGFL（第戎乔治·弗朗索瓦·勒克莱尔中心））；University Hospital of Dijon（第戎大学医院）
Deep learning has shown unprecedented success in a variety of applications, such as computer vision and medical image analysis. However, there is still potential to improve segmentation in multimodal images by embedding prior knowledge via learning-based shape modeling and registration to learn the modality invariant anatomical structure of organs. For example, in radiotherapy automatic prostate segmentation is essential in prostate cancer diagnosis, therapy, and post-therapy assessment from T2-weighted MR or CT images. In this paper, we present a fully automatic deep generative model-driven m…
HistoEncoder: a digital pathology foundation model for prostate cancer
University of Helsinki（赫尔辛基大学）；Helsinki University Hospital（赫尔辛基大学医院）；Skåne University Hospital（斯科讷大学医院）；Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki（赫尔辛基大学HiLIFE项目下属芬兰分子医学研究所（FIMM））；iCAN Digital Precision Cancer Medicine Flagship（iCAN数字精准癌症医学旗舰项目）
In this work we propose to segment the prostate on a challenging dataset of trans-rectal ultrasound (TRUS) images using convolutional neural networks (CNNs) and statistical shape models (SSMs). TRUS is commonly used for a number of image-guided interventions on the prostate. Fast and accurate segmentation on the organ in these images is crucial to planning and fusion with other modalities such as magnetic resonance images (MRIs) . However, TRUS has limited soft tissue contrast and signal to noise ratio which makes the task of segmenting the prostate challenging and subject to inter-observer an…
University Hospital of Marburg (UKGM)（马尔堡大学医院 (UKGM)）
Prostate cancer is the most abundant cancer in men, with over 200,000 expected new cases and around 28,000 deaths in 2012 in the US alone. In this study, the segmentation results for the prostate central gland (PCG) in MR scans are presented. The aim of this research study is to apply a graph-based algorithm to automated segmentation (i.e. delineation) of organ limits for the prostate central gland. The ultimate goal is to apply automated segmentation approach to facilitate efficient MR-guided biopsy and radiation treatment planning. The automated segmentation algorithm used is graph-driven ba…
Research Computing Center at the University of Chicago（芝加哥大学研究计算中心）
Prostate cancer is one of the most common causes of cancer deaths in men. There is a growing demand for noninvasively and accurately diagnostic methods that facilitate the current standard prostate cancer risk assessment in clinical practice. Still, developing computer-aided classification tools in prostate cancer diagnostics from multiparametric magnetic resonance images continues to be a challenge. In this work, we propose a novel deep learning approach for automatic classification of prostate lesions in the corresponding magnetic resonance images by constructing a two-stage multimodal multi…
Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation
Shenzhen University（深圳大学）；Shenzhen University Medical School（深圳大学医学院）；School of Biomedical Engineering（生物医学工程学院）；Huawei Cloud Computing Technologies Company Limited（华为云计算技术有限公司）；Shenzhen University General Hospital（深圳大学总医院）
Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge
Beijing Rossum Robot Technology Co., Ltd.（北京罗素机器人科技有限公司）；Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University（生物力学与机械生物学重点实验室，教育部，北京生物医学创新中心，生物科学与医学工程学院，北航）；Department of Computer Science, Johns Hopkins University（计算机科学系，约翰霍普金斯大学）；Division of Medical Image Computing, German Cancer Research Center (DKFZ)（医学影像计算部，德国癌症研究中心（DKFZ））；Helmholtz Imaging, Heidelberg（海德堡大学医院影像中心）；Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Medical UltraSound Image Computing（智能医学影像、学习与工程（SMILE）实验室，医学超声影像计算）
Brigham and Women's Hospital（布莱根和妇女医院）；Harvard Medical School（哈佛医学院）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）
Histology-based grade classification is clinically important for many cancer types in stratifying patients distinct treatment groups. In prostate cancer, the Gleason score is a grading system used to measure the aggressiveness of prostate cancer from the spatial organization of cells and the distribution of glands. However, the subjective interpretation of Gleason score often suffers from large interobserver and intraobserver variability. Previous work in deep learning-based objective Gleason grading requires manual pixel-level annotation. In this work, we propose a weakly-supervised approach …
Geometrical Cross-Attention and Nonvoid Voxelization for Efficient 3D Medical Image Segmentation
organization= Shenzhen Institute for Advanced Study , addressline= University of Electronic Science；organization= School of Electrical；Electronic Engineering , addressline= Nanyang Technological University , city= Singapore , postcode= 639798 , country= Singapore；organization= School of Science；Engineering , addressline= The Chinese University of Hong Kong, Shenzhen , city= Shenzhen , postcode= 518172 , state= Guangdong , country= China；organization= School of Computer Science；Engineering , addressline= Kyungpook National University , city= Daegu , postcode= 37224 , country= South Korea；organization= Department of Electrical；Computer Engineering , addressline= University of Waterloo , city= Waterloo , postcode= N2L3G1 , state= Ontario , country= Canada
Leiden University Medical Center（莱顿大学医学中心）；Guru Gobind Singh Indraprastha University（古鲁·戈宾德·辛格·因德拉普拉斯塔大学）；Netherlands Cancer Institute（荷兰癌症研究所）
Contouring of the target volume and Organs-At-Risk (OARs) is a crucial step in radiotherapy treatment planning. In an adaptive radiotherapy setting, updated contours need to be generated based on daily imaging. In this work, we leverage personalized anatomical knowledge accumulated over the treatment sessions, to improve the segmentation accuracy of a pre-trained Convolution Neural Network (CNN), for a specific patient. We investigate a transfer learning approach, fine-tuning the baseline CNN model to a specific patient, based on imaging acquired in earlier treatment fractions. The baseline CN…
University College London（伦敦大学学院）；InstaDeep；University College London Hospital NHS Foundation Trust（伦敦大学学院医院NHS基金会信托）
Organ morphology is a key indicator for prostate disease diagnosis and prognosis. For instance, In longitudinal study of prostate cancer patients under active surveillance, the volume, boundary smoothness and their changes are closely monitored on time-series MR image data. In this paper, we describe a new framework for forecasting prostate morphological changes, as the ability to detect such changes earlier than what is currently possible may enable timely treatment or avoiding unnecessary confirmatory biopsies. In this work, an efficient feature-based MR image registration is first developed…
University College London（伦敦大学学院）；University of Oxford（牛津大学）
In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image. Our motion model allows for sampling from the conditional distribution of dense displacement fields, is encoded by a generative neural network conditioned on a medical image, and accepts random noise as additional input. The generative network is trained by a minimax optimisation with a second discriminative neural network, tasked to distinguish generated samples from training motion data. In this work, we propose that 1) jointly optim…
Shiraz University of Technology（设拉子科技大学）；National Brain Mapping Laboratory（国家脑图谱实验室）
Perineural invasion (PNI) by malignant tumor cells has been reported as an independent indicator of poor prognosis in various cancers. Assessment of PNI in small nerves on glass slides is a labor-intensive task. In this study, we propose an algorithm to detect the perineural invasions in colon, prostate, and pancreas cancers based on a convolutional neural network (CNN).
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
Technical University of Munich（慕尼黑工业大学）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛大学医学院麻省总医院阿西诺拉·A.马蒂诺斯生物医学成像中心）；University of Zurich（苏黎世大学）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学TranslaTUM转化癌症研究中心）；McGovern Institute, Massachusetts Institute of Technology（麻省理工学院麦戈文研究所）；Helmholtz AI, Helmholtz Zentrum München（慕尼黑亥姆霍兹中心亥姆霍兹AI）
Wayne State University（韦恩州立大学）；University of Michigan（密歇根大学）；University of Wisconsin Madison（威斯康星大学麦迪逊分校）
In medical image synthesis, model training could be challenging due to the inconsistencies between images of different modalities even with the same patient, typically caused by internal status/tissue changes as different modalities are usually obtained at a different time. This paper proposes a novel deep learning method, Structure-aware Generative Adversarial Network (SA-GAN), that preserves the shapes and locations of in-consistent structures when generating medical images. SA-GAN is employed to generate synthetic computed tomography (synCT) images from magnetic resonance imaging (MRI) with…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Background and purpose: Radiation-induced erectile dysfunction (RiED) is commonly seen in prostate cancer patients. Clinical trials have been developed in multiple institutions to investigate whether dose-sparing to the internal-pudendal-arteries (IPA) will improve retention of sexual potency. The IPA is usually not considered a conventional organ-at-risk (OAR) due to segmentation difficulty. In this work, we propose a deep learning (DL)-based auto-segmentation model for the IPA that utilizes CT and MRI or CT alone as the input image modality to accommodate variation in clinical practice. Mate…
Three-Dimensional Anatomical Data Generation Based on Artificial Neural Networks
Division of Smart Technologies for Tumor Therapy, German Cancer Research Center (DKFZ) Site Dresden（智能肿瘤治疗技术部，德国癌症研究中心（DKFZ）德累斯顿分部）；Faculty of Computer Science, Dresden University of Technology（计算机科学系，德累斯顿技术大学）；Department of Urology, Faculty of Medicine, University of Freiburg - Medical Centre（泌尿科，弗赖堡大学医学中心医学院）；Division of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC) Dresden（转化外科肿瘤学部，国家肿瘤疾病中心（NCT/UCC）德累斯顿分部）；Faculty of Medicine Carl Gustav Carus, Dresden University of Technology（卡尔·古斯塔夫·卡鲁斯医学院，德累斯顿技术大学）；Faculty of Electrical and Computer Engineering, Dresden University of Technology（电气与计算机工程系，德累斯顿技术大学）
University College London（伦敦大学学院）；University of Leeds（利兹大学）
Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible. This has not only been adopted in real-world clinical applications, such as the MR-to-ultrasound registration for prostate intervention of interest in this work, but also provides an explainable means of understanding the organ motion and spatial correspondence establishment. This work instantiates the recently-proposed physics-informed neural networks (PINNs) to a 3D linear elastic model for …
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation
Northwestern University（西北大学）；IIIT Delhi（德里印度信息技术学院）；NSIT Delhi（德里国家信息技术学院）；Space Research Institute (IWF) of Austrian Academy of Sciences（奥地利科学院空间研究所）
Simon Fraser University（西蒙弗雷泽大学）；Siemens Healthineers（西门子医疗）
Simultaneous segmentation of multiple organs from different medical imaging modalities is a crucial task as it can be utilized for computer-aided diagnosis, computer-assisted surgery, and therapy planning. Thanks to the recent advances in deep learning, several deep neural networks for medical image segmentation have been introduced successfully for this purpose. In this paper, we focus on learning a deep multi-organ segmentation network that labels voxels. In particular, we examine the critical choice of a loss function in order to handle the notorious imbalance problem that plagues both the …
University of Waterloo（滑铁卢大学）；Suez Canal University（苏伊士运河大学）
Quantifying the accuracy of segmentation and manual delineation of organs, tissue types and tumors in medical images is a necessary measurement that suffers from multiple problems. One major shortcoming of all accuracy measures is that they neglect the anatomical significance or relevance of different zones within a given segment. Hence, existing accuracy metrics measure the overlap of a given segment with a ground-truth without any anatomical discrimination inside the segment. For instance, if we understand the rectal wall or urethral sphincter as anatomical zones, then current accuracy measu…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Data cleaning consumes about 80% of the time spent on data analysis for clinical research projects. This is a much bigger problem in the era of big data and machine learning in the field of medicine where large volumes of data are being generated. We report an initial effort towards automated patient data cleaning using deep learning: the standardization of organ labeling in radiation therapy. Organs are often labeled inconsistently at different institutions (sometimes even within the same institution) and at different time periods, which poses a problem for clinical research, especially for m…
University College London（伦敦大学学院）；University of Oxford（牛津大学）
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-t…
National Institutes of Health（美国国立卫生研究院）；University of Wisconsin（威斯康星大学）
Current deep learning based segmentation models often generalize poorly between domains due to insufficient training data. In real-world clinical applications, cross-domain image analysis tools are in high demand since medical images from different domains are often needed to achieve a precise diagnosis. An important example in radiology is generalizing from non-contrast CT to contrast enhanced CTs. Contrast enhanced CT scans at different phases are used to enhance certain pathologies or organs. Many existing cross-domain image-to-image translation models have been shown to improve cross-domai…
University College London（伦敦大学学院）；University of Oxford（牛津大学）
Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in …
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）
A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI)
The Institute of Cancer Research（癌症研究所）；The Royal Marsden NHS Foundation Trust（皇家马斯登NHS基金会信托）；University Hospital Basel（巴塞尔大学医院）
École Polytechnique de Montréal（蒙特利尔理工学院）；Montreal Institute for Learning Algorithms（蒙特利尔学习算法研究所）；Imagia Inc.（Imagia公司）；Université de Montréal（蒙特利尔大学）；CHUM Research Center（CHUM研究中心）
In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importance of a trainable pre-processing when using FC-ResNets and we show that a low-capacity FCN model can serve as a pre-processor to normalize medical input data. In our image segmentation pipeline, we use …
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；The Chinese University of Hong Kong（香港中文大学）；Lehigh University（理海大学）；Harvard University（哈佛大学）；The University of Georgia（佐治亚大学）
Technion Israel Institute of Technology（以色列理工学院）；Rambam Health Care Campus（兰巴姆医疗中心）
The integration of artificial intelligence into digital pathology has the potential to automate and improve various tasks, such as image analysis and diagnostic decision-making. Yet, the inherent variability of tissues, together with the need for image labeling, lead to biased datasets that limit the generalizability of algorithms trained on them. One of the emerging solutions for this challenge is synthetic histological images. However, debiasing real datasets require not only generating photorealistic images but also the ability to control the features within them. A common approach is to us…
