RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scans
Department of Industrial Engineering & Operations Research, IIT Bombay, India（印度理工学院孟买分校工业工程与运筹学系）；Department of Energy Science and Engineering, IIT Bombay, India（印度理工学院孟买分校能源科学与工程系）
Weakly Supervised Anomaly detection (WSAD) in brain MRI scans is an important challenge useful to obtain quick and accurate detection of brain anomalies when precise pixel-level anomaly annotations are unavailable and only weak labels (e.g., slice-level) are available. In this work, we propose RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings, a novel two-stage WSAD framework. In the first stage, we introduce a Discriminative Dual Prompt Tuning (DDPT) mechanism that generates high-quality pseudo weak masks based on slice-level labels, serving as coarse localization cues. In the second stage, we propose a segmentation network with a region-aware spatial attention mechanism that relies on fixed location-based random embeddings. This design enables the model to effectively focus on anomalous regions. Our approach achieves state-of-the-art anomaly detection performance, significantly outperforming existing WSAD methods while utilizing less than 8 million parameters. Extensive evaluations on the BraTS20, BraTS21, BraTS23, and MSD datasets demonstrate a substantial performance improvement coupled with a significant reduction in computational complexity. Code is available at: https://github.com/BheeshmSharma/RASALoRE-BMVC-2025/.
Hosei University（法政大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
To build a robust and practical content-based image retrieval (CBIR) system that is applicable to a clinical brain MRI database, we propose a new framework -- Disease-oriented image embedding with pseudo-scanner standardization (DI-PSS) -- that consists of two core techniques, data harmonization and a dimension reduction algorithm. Our DI-PSS uses skull stripping and CycleGAN-based image transformations that map to a standard brain followed by transformation into a brain image taken with a given reference scanner. Then, our 3D convolutioinal autoencoders (3D-CAE) with deep metric learning acqu…
Saarland University（萨尔兰大学）；DFKI（德国人工智能研究中心）；INRIA（法国国家信息与自动化研究所）；University of Edinburgh（爱丁堡大学）
We describe a minimally-supervised method for computing a statistical shape space model of the palate surface. The model is created from a corpus of volumetric magnetic resonance imaging (MRI) scans collected from 12 speakers. We extract a 3D mesh of the palate from each speaker, then train the model using principal component analysis (PCA). The palate model is then tested using 3D MRI from another corpus and evaluated using a high-resolution optical scan. We find that the error is low even when only a handful of measured coordinates are available. In both cases, our approach yields promising …
Recent studies suggest that combined analysis of Magnetic resonance imaging~(MRI) that measures brain atrophy and positron emission tomography~(PET) that quantifies hypo-metabolism provides improved accuracy in diagnosing Alzheimer's disease. However, such techniques are limited by the availability of corresponding scans of each modality. Current work focuses on a cross-modal approach to estimate FDG-PET scans for the given MR scans using a 3D U-Net architecture. The use of the complete MR image instead of a local patch based approach helps in capturing non-local and non-linear correlations be…
Technical University of Munich（慕尼黑工业大学）；Ludwig-Maximilians-University Munich（慕尼黑路德维希-马克西米利安大学）
The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology. Although traditional and deep learning-based algorithmic pipelines exist for this purpose, they have two major drawbacks: lengthy runtimes of multiple hours (traditional) or intricate post-processing, such as mesh extraction and topology correction (deep learning-based). In this work, we address both of these issues and propose Vox2Cortex, a deep learning-based algorithm that directly yields topologically correct, three-di…
Ibn Tofail University（伊本·托法勒大学）；Cheikh Anta Diop University（谢赫·安塔·迪奥普大学）；Abdelmalek Essaadi University（阿卜杜勒马利克·埃萨迪大学）
Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of irregularity of the medical images. This descriptor property does not give ownership of the local image structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM Features. This powerful combination has proved good results especially in classification of medical texture from MRI and CT Scan images of trabecular bone. This method has the potentia…
Tel Aviv Medical Center（特拉维夫医疗中心）；Tel Aviv University（特拉维夫大学）
Purpose: Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans. Methods: The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies. Since similarity is not guaranteed, sampling and reconstruction are adjusted during acquisition to match the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, …
School of Biomedical Engineering, King’s College London（伦敦国王学院生物医学工程学院）；King’s College Hospital（国王学院医院）；Institute of Psychiatry, Psychology & Neuroscience, King’s College London（伦敦国王学院精神病学、心理学与神经科学研究所）；Centre for Medical Image Computing, Dementia Research, University College London（伦敦大学学院医学影像计算中心（痴呆研究））
Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label the…
Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique. However, acquiring high signal-to-noise ratio MRI scans requires long scan times, resulting in increased costs and patient discomfort, and decreased throughput. Thus, there is great interest in denoising MRI scans, especially for the subtype of diffusion MRI scans that are severely SNR-limited. While most prior MRI denoising methods are supervised in nature, acquiring supervised training datasets for the multitude of anatomies, MRI scanners, and scan parameters proves impractical. Here, we propose Denoising…
Biomediq A/S（Biomediq公司）；University of Copenhagen（哥本哈根大学）
Scanner drift is a well-known magnetic resonance imaging (MRI) artifact characterized by gradual signal degradation and scan intensity changes over time. In addition, hardware and software updates may imply abrupt changes in signal. The combined effects are particularly challenging for automatic image analysis methods used in longitudinal studies. The implication is increased measurement variation and a risk of bias in the estimations (e.g. in the volume change for a structure). We proposed two quite different approaches for scanner drift normalization and demonstrated the performance for segm…
A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of 'free' self-supervision for training a deep network. We demonstrate this self-supervised learning for the case of T2-weighted sagittal lumbar Magnetic Resonance Images (MRIs). A Siamese convolutional neural network (CNN) is trained using two losses: (i) a contrastive loss on whether the scan is of the same person (i.e. longitudinal) or not, together with (ii) a classification loss on predicting the level of vertebral bodies. T…
Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available for diagnosis. Yet, excessive scan times associated with additional contrasts may be a limiting factor. Two mainstream approaches for enhanced scan efficiency are reconstruction of undersampled acquisitions and synthesis of missing acquisitions. In reconstruction, performance decreases towards higher acceleration factors with diminished sampling density particularly at high-spatial-frequencies. In synthesis, the absence of data samples from the target contrast can lead to artefactual sensitivity or inse…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Incyte Corporation（因赛特公司）
The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen disease. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to (1) large anatomical and spatial variations of splenomegaly, (2) large inter- and intra-scan intensity variations on multi-modal MRI, and (3) limited numbers of labeled splenomegaly scans. In this paper, we propose the Splenomegaly Segmentation Network (SS-Net) to intro…
Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；California Institute of Technology（加州理工学院）；Massachusetts Institute of Technology（麻省理工学院）
In neuroimaging, MRI tissue properties characterize underlying neurobiology, provide quantitative biomarkers for neurological disease detection and analysis, and can be used to synthesize arbitrary MRI contrasts. Estimating tissue properties from a single scan session using a protocol available on all clinical scanners promises to reduce scan time and cost, enable quantitative analysis in routine clinical scans and provide scan-independent biomarkers of disease. However, existing tissue properties estimation methods - most often $\mathbf{T_1}$ relaxation, $\mathbf{T_2^*}$ relaxation, and proto…
Centro Algoritmi, University of Minho（米尼奥大学算法中心）；Université Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin（巴黎萨克雷大学、法国原子能与可替代能源委员会、法国国家科学研究中心、BAOBAB、NeuroSpin）
Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are the availability of MRI scanners and the time required for a full scanning session (it usually takes over 30 minutes). Privacy laws and the 3Rs ethics rule also make it difficult to create large datasets for training deep learning models. Generative Adversarial Networks (GANs) can perform data augmentation with higher quality than other techniques…
SGAM: Shared Gaussian Geometry with Implicit Amplitude Modeling for Scan-Specific 3D Multi-Contrast MRI Reconstruction
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院劳特伯生物医学成像研究中心）
Motion artefacts created by patient motion during an MRI scan occur frequently in practice, often rendering the scans clinically unusable and requiring a re-scan. While many methods have been employed to ameliorate the effects of patient motion, these often fall short in practice. In this paper we propose a novel method for removing motion artefacts using a deep neural network with two input branches that discriminates between patient poses using the motion's timing. The first branch receives a subset of the $k$-space data collected during a single patient pose, and the second branch receives …
Brain Tumor Classification from MRI Scans via Transfer Learning and Enhanced Feature Representation
Department of CSE, Netrokona University（计算机科学与工程系，Netrokona大学）；Department of CSE, The University of Rajshahi（计算机科学与工程系，拉贾沙希大学）；Department of Information Technology, Allen E. Paulson College of Engineering and Computing, Georgia Southern University（信息技术系，乔治亚南方大学）；Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University（图像与视觉计算中心（CIVC），人工智能学院，人工智能与工程学院，多媒体大学）
The Hong Kong University of Science and Technology（香港科技大学）
Registration of pre-operative and follow-up brain MRI scans is challenging due to the large variation of tissue appearance and missing correspondences in tumour recurrence regions caused by tumour mass effect. Although recent deep learning-based deformable registration methods have achieved remarkable success in various medical applications, most of them are not capable of registering images with pathologies. In this paper, we propose a 3-step registration pipeline for pre-operative and follow-up brain MRI scans that consists of 1) a multi-level affine registration, 2) a conditional deep Lapla…
This work addresses the problem of estimating proton density and T1 maps from two partially sampled K-space scans such that the total acquisition time remains approximately the same as a single scan. Existing multi parametric non linear curve fitting techniques require a large number (8 or more) of echoes to estimate the maps resulting in prolonged (clinically infeasible) acquisition times. Our simulation results show that our method yields very accurate and robust results from only two partially sampled scans (total scan time being the same as a single echo MRI). We model PD and T1 maps to be…
University College London（伦敦大学学院）；University of Washington（华盛顿大学）
Neuroimaging to neuropathology correlation (NTNC) promises to enable the transfer of microscopic signatures of pathology to in vivo imaging with MRI, ultimately enhancing clinical care. NTNC traditionally requires a volumetric MRI scan, acquired either ex vivo or a short time prior to death. Unfortunately, ex vivo MRI is difficult and costly, and recent premortem scans of sufficient quality are seldom available. To bridge this gap, we present methodology to 3D reconstruct and segment full brain image volumes from brain dissection photographs, which are routinely acquired at many brain banks an…
International University of Business Agriculture and Technology（国际商业、农业与技术大学）
Tissue classification is one of the significant tasks in the field of biomedical image analysis. Magnetic Resonance Imaging (MRI) is of great importance in tissue classification especially in the areas of brain tissue classification which is able to recognize anatomical areas of interest such as surgical planning, monitoring therapy, clinical drug trials, image registration, stereotactic neurosurgery, radiotherapy etc. The task of this paper is to implement different unsupervised classification algorithms in ITK and perform tissue classification (white matter, gray matter, cerebrospinal fluid …
Parkinsons disease, PD, is a chronic condition that affects motor skills and includes symptoms like tremors and rigidity. The current diagnostic procedure uses patient assessments to evaluate symptoms and sometimes a magnetic resonance imaging or MRI scan. However, symptom variations cause inaccurate assessments, and the analysis of MRI scans requires experienced specialists. This research proposes to accurately diagnose PD severity with deep learning by combining symptoms data and MRI data from the Parkinsons Progression Markers Initiative database. A new hybrid model architecture was impleme…
WebMRIQC: A Web-Based Implementation of MRIQC for Accessible MRI Image Quality Assessment in Resource-Constrained Settings
University of Lagos（拉各斯大学）；McGill University（麦吉尔大学）；Jomo Kenyatta University of Agriculture and Technology（乔莫·肯雅塔农业技术大学）；Federal University of Health Sciences, Azare（阿扎雷联邦健康科学大学）；Muhimbili Orthopaedic Institute（穆希比利骨科研究所）；Crestview Radiology Ltd.（克雷斯特维尤放射有限公司）
Parallel imaging is a widely-used technique to accelerate magnetic resonance imaging (MRI). However, current methods still perform poorly in reconstructing artifact-free MRI images from highly undersampled k-space data. Recently, implicit neural representation (INR) has emerged as a new deep learning paradigm for learning the internal continuity of an object. In this study, we adopted INR to parallel MRI reconstruction. The MRI image was modeled as a continuous function of spatial coordinates. This function was parameterized by a neural network and learned directly from the measured k-space it…
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Glioblastoma is a common brain malignancy that tends to occur in older adults and is almost always lethal. The effectiveness of chemotherapy, being the standard treatment for most cancer types, can be improved if a particular genetic sequence in the tumor known as MGMT promoter is methylated. However, to identify the state of the MGMT promoter, the conventional approach is to perform a biopsy for genetic analysis, which is time and effort consuming. A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor and hence suggested the use o…
Indian Institute of Technology Indore（印度理工学院印多尔分校）
Alzheimer's Disease (AD) is a neurodegenerative disease that is one of the significant causes of death in the elderly population. Many deep learning techniques have been proposed to diagnose AD using Magnetic Resonance Imaging (MRI) scans. Predicting AD using 2D slices extracted from 3D MRI scans is challenging as the inter-slice information gets lost. To this end, we propose a novel and lightweight framework termed 'Biceph-Net' for AD diagnosis using 2D MRI scans that model both the intra-slice and inter-slice information. Biceph-Net has been experimentally shown to perform similar to other S…
A generalisable head MRI defacing pipeline: Evaluation on 2,566 meningioma scans
School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学校）；Department of Neurosurgery, King’s College Hospital NHS Foundation Trust（伦敦国王学院医院神经外科部门）
MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction
Department of Bioengineering and I-X, Imperial College London（生物工程系和I-X，帝国理工学院伦敦分校）；Cardiovascular Research Centre, Royal Brompton Hospital（心脏血管研究中心，皇家布隆特医院）；National Heart and Lung Institute, Imperial College London（国家心脏和肺研究所，帝国理工学院伦敦分校）；School of Biomedical Engineering & Imaging Sciences, King’s College London（生物医学工程与成像科学学院，伦敦国王学院）；Shanghai Pudong Hospital and Human Phenome Institute, Fudan University（上海浦东医院和人类表型研究所，复旦大学）；International Human Phenome Institute (Shanghai), Shanghai, China（国际人类表型研究所（上海），上海，中国）
King’s College London（伦敦国王学院）；King’s College Hospital（国王学院医院）；Wrightington, Wigan and Leigh NHSFT（赖廷顿、维冈与利国家医疗服务基金会信托）；University College London（伦敦大学学院）
The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in $\text{T}_2$-…
University College London（伦敦大学学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Copenhagen University Hospital（哥本哈根大学医院）；Technical University of Denmark（丹麦技术大学）；Massachusetts Institute of Technology（麻省理工学院）
Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are highly sensitive to changes in resolution and contrast: even within the same MRI modality, performance can decrease across datasets. Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution. SynthSeg is trained with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation strategy where we fully randomise the …
Department of Electrical and Computer Engineering, University of Minnesota（明尼苏达大学电气与计算机工程系）
Long scan duration remains a challenge for high-resolution MRI. Deep learning has emerged as a powerful means for accelerated MRI reconstruction by providing data-driven regularizers that are directly learned from data. These data-driven priors typically remain unchanged for future data in the testing phase once they are learned during training. In this study, we propose to use a transfer learning approach to fine-tune these regularizers for new subjects using a self-supervision approach. While the proposed approach can compromise the extremely fast reconstruction time of deep learning MRI met…
University of Brescia（布雷西亚大学）；University of Glasgow（格拉斯哥大学）
Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Recently, methods deploying solutions based on Convolutional Neural Networks (CNNs) are making the direct analysis of out-of-the-scanner data feasible. However, current CNN-based solutions partition the t…
University College London（伦敦大学学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e.g., MP-RAGE). This limitation prevents the analysis of millions of MRI scans acquired with large inter-slice spacing ("thick slice") in clinical settings every year. The inability to quantitatively analyze these scans hinders the adoption of quantitative neuroimaging in healthcare, and precludes research studies that could attain huge sample sizes and h…
University of Karachi（卡拉奇大学）；Zhejiang University（浙江大学）；Iqra University（伊克拉大学）；Karachi Institute of Economics and Technology（卡拉奇经济与技术学院）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）
Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation of the organ of interest. However, for the segmentation of heart using MRI, only few methods have been proposed each with its own merits and demerits. For further advancement in this area of research, we analyze automated heart segmentation methods …
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
Charité - Universitätsmedizin Berlin（柏林查理医院）；Freie Universität Berlin（柏林自由大学）；Humboldt Universität zu Berlin（柏林洪堡大学）；Radboud University Medical Center（拉德伯德大学医学中心）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Technical University of Munich（慕尼黑技术大学）
Evaluation of Machine Learning Reconstruction Techniques for Accelerated Brain MRI Scans
Humber River Hospital（亨伯河医院）；Toronto Western Hospital, University of Toronto（多伦多大学多伦多西区医院）；Foqus Technologies Inc.（Foqus 技术公司）；Robarts Research Institute, Western University（西安大略大学罗巴茨研究所）
University of Oxford（牛津大学）；Plexalis Ltd（Plexalis有限公司）
We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies and convolutional image-to-image translation followed by beam search to label vertebral levels in a self-consistent manner. The method can be applied without modification to lumbar, cervical and thoracic-only scans across a range of different MR sequences. The resulting system achieves 98.1% detection rate and 96.5% identification rate on a challenging clinical datas…
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 …
Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases. Automatic segmentation of brain tumors is a challenging task for computer-aided diagnosis due to low-tissue contrast in the tumor subregions. To overcome this, we devise a novel pixel-wise segmentation framework through a convolutional 3D to 2D MR patch conversion model to predict class labels of the central pixel in the input sliding patches. Precisely, we first extract 3D patches from each modality to calibrate slices through the squeeze and excitation (SE) block. Then, the outp…
Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Copenhagen University Hospital - Amager and Hvidovre（哥本哈根大学医院阿莫尔与维杜夫院区）；Danish Research Centre for Magnetic Resonance（丹麦磁共振研究中心）；Centre for Functional and Diagnostic Imaging and Research（功能与诊断成像及研究中心）
University of Stuttgart（斯图加特大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Age is an essential factor in modern diagnostic procedures. However, assessment of the true biological age (BA) remains a daunting task due to the lack of reference ground-truth labels. Current BA estimation approaches are either restricted to skeletal images or rely on non-imaging modalities that yield a whole-body BA assessment. However, various organ systems may exhibit different aging characteristics due to lifestyle and genetic factors. In this initial study, we propose a new framework for organ-specific BA estimation utilizing 3D magnetic resonance image (MRI) scans. As a first step, thi…
