Fully convolutional networks (FCNs), including UNet and VNet, are widely-used network architectures for semantic segmentation in recent studies. However, conventional FCN is typically trained by the cross-entropy or Dice loss, which only calculates the error between predictions and ground-truth labels for pixels individually. This often results in non-smooth neighborhoods in the predicted segmentation. To address this problem, we propose a two-stage framework, with the first stage to quickly localize the prostate region and the second stage to precisely segment the prostate by a multi-task UNet architecture. We introduce a novel online metric learning module through voxel-wise sampling in the multi-task network. Therefore, the proposed network has a dual-branch architecture that tackles two tasks: 1) a segmentation sub-network aiming to generate the prostate segmentation, and 2) a voxel-metric learning sub-network aiming to improve the quality of the learned feature space supervised by a metric loss. Specifically, the voxel-metric learning sub-network samples tuples (including triplets and pairs) in voxel-level through the intermediate feature maps. Unlike conventional deep metric learning methods that generate triplets or pairs in image-level before the training phase, our proposed voxel-wise tuples are sampled in an online manner and operated in an end-to-end fashion via multi-task learning. To evaluate the proposed method, we implement extensive experiments on a real CT image dataset consisting of 339 patients. The ablation studies show that our method can effectively learn more representative voxel-level features compared with the conventional learning methods with cross-entropy or Dice loss. And the comparisons show that the proposed method outperforms the state-of-the-art methods by a reasonable margin.
City University of Hong Kong（香港城市大学）；University College London（伦敦大学学院）
Dice similarity coefficient (DSC) and Hausdorff distance (HD) are widely used for evaluating medical image segmentation. They have also been criticised, when reported alone, for their unclear or even misleading clinical interpretation. DSCs may also differ substantially from HDs, due to boundary smoothness or multiple regions of interest (ROIs) within a subject. More importantly, either metric can also have a nonlinear, non-monotonic relationship with outcomes based on Type 1 and 2 errors, designed for specific clinical decisions that use the resulting segmentation. Whilst cases causing disagr…
While current research has shown the importance of Multi-parametric MRI (mpMRI) in diagnosing prostate cancer (PCa), further investigation is needed for how to incorporate the specific structures of the mpMRI data, such as the regional heterogeneity and between-voxel correlation within a subject. This paper proposes a machine learning-based method for improved voxel-wise PCa classification by taking into account the unique structures of the data. We propose a multi-resolution modeling approach to account for regional heterogeneity, where base learners trained locally at multiple resolutions ar…
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…
University of Magdeburg（马格德堡大学）；University Hospital Magdeburg（马格德堡大学医院）
Recent studies demonstrated the eligibility of convolutional neural networks (CNNs) for solving the image registration problem. CNNs enable faster transformation estimation and greater generalization capability needed for better support during medical interventions. Conventional fully-supervised training requires a lot of high-quality ground truth data such as voxel-to-voxel transformations, which typically are attained in a too tedious and error-prone manner. In our work, we use weakly-supervised learning, which optimizes the model indirectly only via segmentation masks that are a more access…
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
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）
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,…
Post-implant dosimetry (PID) is an essential step of prostate brachytherapy that utilizes CT to image the prostate and allow the location and dose distribution of the radioactive seeds to be directly related to the actual prostate. However, it it a very challenging task to identify these seeds in CT images due to the severe metal artifacts and high-overlapped appearance when multiple seeds clustered together. In this paper, we propose an automatic and efficient algorithm based on 3D deep fully convolutional network for identifying implanted seeds in CT images. Our method models the seed locali…
Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation
Nanjing University（南京大学）；Southeast University（东南大学）；State Key Laboratory for Novel Software Technology（计算机软件新技术国家重点实验室）；National Institute of Healthcare Data Science（国家医疗数据科学研究院）
Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation
UCL Hawkes Institute（UCL Hawkes Institute；医学物理与生物医学工程系）；Department of Medical Physics and Biomedical Engineering（大学学院伦敦）；University College London（医学成像中心）；Centre of Medical Imaging（肿瘤学系）；Department of Oncology（阿迦·柯尔大学医院）；Aga Khan University Hospital（生物工程中心；工程与材料科学学院）；Centre for Bioengineering；School of Engineering and Materials Science
3DTMDet: A Dual-Path Synergy Network of Transformer and SSM for 3D Object Detection in Point Clouds
School of Electronic and Optical Engineering（电子与光学工程学院）；The 28th Research Institute of China Electronics Technology Group Corporation（中国电子科技集团第二十八研究所）；College of Astronautics（航天学院）；School of Information and Communication Engineering（信息与通信工程学院）；State key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument（极端环境光电动态测量技术与仪器国家重点实验室）
University College London（伦敦大学学院）；University of Oxford（牛津大学）；Siemens Healthineers（西门子医疗）；King’s College London（伦敦国王学院）
Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned. Current learning-based registration methods have adopted the same paradigm and typically predict, for any new input image pair, dense correspondences in the form of a dense displacement field or parameters of a spatial transformation model. However, in many applications of registration, the spatial transformation itself is only required to propagate points or regions of interest (ROIs). In such cases, detaile…
On the Degrees of Freedom of Gridded Control Points in Learning-Based Medical Image Registration
UCL Hawkes Institute（UCL霍克斯研究所；医学物理与生物医学工程系，伦敦大学学院）；Department of Medical Physics and Biomedical Engineering, University College London（医学成像中心，医学系，伦敦大学学院）；Centre for Medical Imaging, Division of Medicine, University College London（外科与介入科学系，伦敦大学学院）；Division of Surgery and Interventional Science, University College London（癌症研究所，泌尿科，UCL医院，伦敦大学学院）；Cancer Institute, Urology Department, UCL Hospital, University College London（放射科，帝国理工医疗）；Radiology Department, Imperial College Healthcare
