Prostate Lesion Estimation using Prostate Masks from Biparametric MRI
Istanbul Technical University（伊斯坦布尔理工大学）；Acibadem Mehmet Ali Aydinlar University School of Medicine（阿吉巴德姆·穆罕默德·阿里·艾丁拉尔大学医学院）；Bahcesehir University（巴耶塞特大学）
Ahmet Karagoz, Mustafa Ege Seker, Mert Yergin, Tarkan Atak Kan, Mustafa Said Kartal, Ercan Karaarslan, Deniz Alis, Ilkay Oksuz
Biparametric MRI has emerged as an alternative to multiparametric prostate MRI, which eliminates the need for the potential harms to the patient due to the contrast medium. One major issue with biparametric MRI is difficulty to detect clinically significant prostate cancer (csPCA). Deep learning algorithms have emerged as an alternative solution to detect csPCA in cohort studies. We present a workflow which predicts csPCA on biparametric prostate MRI PI-CAI 2022 Challenge with over 10,000 carefully-curated prostate MRI exams. We propose to to segment the prostate gland first to the central gland (transition + central zone) and the peripheral gland. Then we utilize these predcitions in combination with T2, ADC and DWI images to train an ensemble nnU-Net model. Finally, we utilize clinical indices PSA and ADC intensity distributions of lesion regions to reduce the false positives. Our method achieves top results on open-validation stage with a AUROC of 0.888 and AP of 0.732.
Purpose: We aimed to develop deep machine learning (DL) models to improve the detection and segmentation of intraprostatic lesions (IL) on bp-MRI by using whole amount prostatectomy specimen-based delineations. We also aimed to investigate whether transfer learning and self-training would improve results with small amount labelled data. Methods: 158 patients had suspicious lesions delineated on MRI based on bp-MRI, 64 patients had ILs delineated on MRI based on whole mount prostatectomy specimen sections, 40 patients were unlabelled. A non-local Mask R-CNN was proposed to improve the segmentat…
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London（医学图像计算中心，医学物理与生物医学工程系，伦敦大学学院）；Div of Surgery & Interventional Sci, University College London（外科与介入科学部，伦敦大学学院）；School of Computer Science and Engineering, University of Westminster（计算机科学与工程学院，威斯敏斯特大学）
The diagnosis of prostate cancer faces a problem with overdiagnosis that leads to damaging side effects due to unnecessary treatment. Research has shown that the use of multi-parametric magnetic resonance images to conduct biopsies can drastically help to mitigate the overdiagnosis, thus reducing the side effects on healthy patients. This study aims to investigate the use of deep learning techniques to explore computer-aid diagnosis based on MRI as input. Several diagnosis problems ranging from classification of lesions as being clinically significant or not to the detection and segmentation o…
Prostate cancer (PCa) is the most common cancer in men in the United States. Multiparametic magnetic resonance imaging (mp-MRI) has been explored by many researchers to targeted prostate biopsies and radiation therapy. However, assessment on mp-MRI can be subjective, development of computer-aided diagnosis systems to automatically delineate the prostate gland and the intraprostratic lesions (ILs) becomes important to facilitate with radiologists in clinical practice. In this paper, we first study the implementation of the Mask-RCNN model to segment the prostate and ILs. We trained and evaluate…
Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）
Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images. The combination of the difficulties in accessing and obtaining large amounts of labeled data and the availability of unlabelled data in the medical imaging domain makes MIM an interesting approach to advance deep learning (DL) applications based on 3D medical imaging data. Nevertheless, SSL and, in particular, MIM applications with medical imaging data are rather scarce …
FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；Siemens Medical Solutions USA（美国西门子医疗解决方案公司）；Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）
Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection
National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）；Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心）；Center for Cancer Research, National Cancer Institute（美国国家癌症研究所癌症研究中心）
The MRI Institute for Biomedical Research（MRI 生物医学研究所）；Uber Technologies Inc.（优步技术公司）；LinkedIn（领英公司）；USAA（美国汽车协会联合服务银行）
A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MRI data provided by the PROSTATEx challenge. End-to-end training was performed for XmasNet, with data augmentation done through 3D rotation and slicing, in order to incorporate the 3D information of the lesion. XmasNet outperformed traditional machine learning models based on engineered features, for both train and test data. For the test data, XmasNet outperformed 69 methods from 33 participating groups and achieved …
Magnetic Resonance Imaging (MRI) is widely used for screening and staging prostate cancer. However, many prostate cancers have subtle features which are not easily identifiable on MRI, resulting in missed diagnoses and alarming variability in radiologist interpretation. Machine learning models have been developed in an effort to improve cancer identification, but current models localize cancer using MRI-derived features, while failing to consider the disease pathology characteristics observed on resected tissue. In this paper, we propose CorrSigNet, an automated two-step model that localizes p…
In this work, we propose a deep U-Net based model to tackle the challenging task of prostate cancer segmentation by aggressiveness in MRI based on weak scribble annotations. This model extends the size constraint loss proposed by Kervadec et al. 1 in the context of multiclass detection and segmentation task. This model is of high clinical interest as it allows training on prostate biopsy samples and avoids time-consuming full annotation process. Performance is assessed on a private dataset (219 patients) where the full ground truth is available as well as on the ProstateX-2 challenge database,…
University of Stavanger（斯塔万格大学）；Stavanger University Hospital（斯塔万格大学医院）；University of Agder（阿格德尔大学）
Prostate cancer (PCa) is the second most common cancer diagnosed among men worldwide. The current PCa diagnostic pathway comes at the cost of substantial overdiagnosis, leading to unnecessary treatment and further testing. Bi-parametric magnetic resonance imaging (bp-MRI) based on apparent diffusion coefficient maps (ADC) and T2-weighted (T2w) sequences has been proposed as a triage test to differentiate between clinically significant (cS) and non-clinically significant (ncS) prostate lesions. However, analysis of the sequences relies on expertise, requires specialized training, and suffers fr…
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…
Mansoura University（曼苏拉大学）；Higher Institute of Engineering and Technology（高等工程技术学院）
Prostate cancer is the most dangerous cancer diagnosed in men worldwide. Prostate diagnosis has been affected by many factors, such as lesion complexity, observer visibility, and variability. Many techniques based on Magnetic Resonance Imaging (MRI) have been used for prostate cancer identification and classification in the last few decades. Developing these techniques is crucial and has a great medical effect because they improve the treatment benefits and the chance of patients' survival. A new technique that depends on MRI has been proposed to improve the diagnosis. This technique consists …
Facebook（脸书）；Stanford University School of Medicine（斯坦福大学医学院）；Stanford University（斯坦福大学）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人医疗保健系统）
Prostate cancer is the second deadliest cancer for American men. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosis, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements. Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. In this study, we…
Interpretable Prostate Cancer Detection using a Small Cohort of MRI Images
Department of Computer Science, Boston University Metropolitan College（波士顿大学计算机科学系）；Department of Radiology, McMaster University（麦斯特大学放射科）；Department of Radiology, School of Medicine, 5th Azar Hospital, Golestan University of Medical Sciences（戈兰大学医学学院放射科，第五阿扎尔医院）；Department of Medical Imaging, Niagara Health System（尼亚加拉健康系统医学影像科）；Center of Excellence in Precision Medicine and Digital Health, Department of Physiology, Chulalongkorn University（朱拉隆梭大学精准医学与数字健康中心，生理学系）
A multi-architecture study of specificity refinement and false-positive mechanism analysis in prostate MRI
The Second Xiangya Hospital of Central South University（中南大学湘雅医学院第二医院）；School of Life Sciences, Central South University（中南大学生命科学学院）；Hunan Provincial Key Laboratory of Medical Information Research (Central South University)（湖南省医学信息研究重点实验室（中南大学））；Hunan Provincial Clinical Medical Research Center for Cardiovascular Intelligent Medicine（湖南省心血管智能医学临床医学研究中心）
Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer
College of Artificial Intelligence, Nankai University（南开大学人工智能学院）；Tianjin First Central Hospital（天津市第一中心医院）；School of Electronics and Information Engineering, Tiangong University（天津工业大学电子与信息工程学院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；North China Digital Health Technology Co., Ltd.（华北数字健康科技有限公司）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
To our knowledge, all deep computer-aided detection and diagnosis (CAD) systems for prostate cancer (PCa) detection consider bi-parametric magnetic resonance imaging (bp-MRI) only, including T2w and ADC sequences while excluding the 4D perfusion sequence,which is however part of standard clinical protocols for this diagnostic task. In this paper, we question strategies to integrate information from perfusion imaging in deep neural architectures. To do so, we evaluate several ways to encode the perfusion information in a U-Net like architecture, also considering early versus mid fusion strategi…
A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属仁济医院）；National University of Singapore（新加坡国立大学）；Shanghai East Hospital, Tongji University School of Medicine（同济大学医学院附属东方医院）；McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth Houston)（德克萨斯大学休斯顿健康科学中心麦克威廉姆斯生物信息学院）；School of Transportation, Southeast University（东南大学交通学院）；Ningbo Hangzhou Bay Hospital（宁波杭州湾医院）；Changhai Hospital, Naval Medical University（海军军医大学长海医院）
Segmentation Strategies in Deep Learning for Prostate Cancer Diagnosis: A Comparative Study of Mamba, SAM, and YOLO
School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院）；School of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程学院）；School of Electrical Engineering, Shahrood Branch, Islamic Azad University（伊斯兰阿扎德大学沙赫鲁德分校电气工程学院）；Medical Sciences and Technologies Department, Science and Research Branch, Islamic Azad University（伊斯兰阿扎德大学科学研究分支医学与技术系）；Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院生物医学工程系）
Z-SSMNet: Zonal-aware Self-supervised Mesh Network for Prostate Cancer Detection and Diagnosis with Bi-parametric MRI
School of Computer Science, Faculty of Engineering, The University of Sydney（悉尼大学工程学院计算机科学学院）；College of Science and Engineering, James Cook University（詹姆斯库克大学科学与工程学院）；Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Nepean Hospital（尼平医院）；Telehealth and Technology Centre, Nepean Blue Mountains Local Health District (NBMLHD)（尼平蓝山地方卫生区远程医疗与技术中心）
Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging
UCL Hawkes Institute and the Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学学院霍克斯研究所与医学物理与生物医学工程系）；Centre for Bioengineering, School of Engineering and Materials Science and Digital Environment Research Institute, Queen Mary University of London（伦敦玛丽女王大学工程与材料科学学院生物工程中心与数字环境研究所）；Centre for Medical Imaging, University College London（伦敦大学学院医学影像中心）；Division of Surgery & Interventional Science, University College London（伦敦大学学院外科与介入科学部）；Urological Research Network（泌尿研究网络）
Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation
Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong（香港大学李嘉诚医学院诊断放射学系）；Department of Pathology, Li Ka Shing Faculty of Medicine, The University of Hong Kong（香港大学李嘉诚医学院病理学系）；Department of Anatomical and Cellular Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong（香港中文大学威尔斯亲王医院解剖及细胞病理学系）；Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong（香港城市大学赛马会动物医学及生命科学院传染病及公共卫生学系）；CityU Veterinary Diagnostic Laboratory Co., Ltd., City University of Hong Kong（香港城市大学城市大学兽医诊断实验室有限公司）
How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models
BC Cancer Research Institute（BC癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）；BC Cancer（BC癌症中心）
The emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), which is the most prevalent malignancy in males in the western world, enabling a better selection of patients for confirmation biopsy. However, analyzing these images is complex even for experts, hence opening an opportunity for computer-aided diagnosis systems to seize. This paper proposes a fully automatic system based on Deep Learning that takes a prostate mpMRI from a PCa-suspect patient and, by leveraging the Retina U-Net detection framework, locates P…
Huazhong University of Science and Technology（华中科技大学）；Hong Kong University of Science and Technology（香港科技大学）
In this paper, we address the problem of synthesizing multi-parameter magnetic resonance imaging (mp-MRI) data, i.e. Apparent Diffusion Coefficients (ADC) and T2-weighted (T2w), containing clinically significant (CS) prostate cancer (PCa) via semi-supervised adversarial learning. Specifically, our synthesizer generates mp-MRI data in a sequential manner: first generating ADC maps from 128-d latent vectors, followed by translating them to the T2w images. The synthesizer is trained in a semisupervised manner. In the supervised training process, a limited amount of paired ADC-T2w images and the c…
Airdoc（鹰瞳科技）；Harbin Engineering University（哈尔滨工程大学）；IIAI（国际人工智能研究院）；Monash University（莫纳什大学）；The University of Sydney（悉尼大学）
Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognition even under a high noisy rate. In medical applications, learning from datasets with label noise is more challenging since medical imaging datasets tend to have asymmetric (class-dependent) noise and suffer from high observer variability. In this paper, we systematically discuss and define the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-…
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 …
