Papers Organ Segmentation
“Organ Segmentation” 태그가 달린 논문 305편 · 필터 해제
GroundingDINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models
Accurate and generalizable object segmentation in ultrasound imaging remains a significant challenge due to anatomical variability, diverse imaging protocols, and limited annotated data. In this study, we propose a promp…
Organ SegmentationSegmentationSemantic SegmentationCRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation
Multi-organ medical segmentation is a crucial component of medical image processing, essential for doctors to make accurate diagnoses and develop effective treatment plans. Despite significant progress in this field, cur…
Organ SegmentationDM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling
Accurate 3D medical image segmentation demands architectures capable of reconciling global context modeling with spatial topology preservation. While State Space Models (SSMs) like Mamba show potential for sequence model…
AnatomyBrain Tumor SegmentationDecoderImage Segmentation+7CENet: Context Enhancement Network for Medical Image Segmentation
Medical image segmentation, particularly in multi-domain scenarios, requires precise preservation of anatomical structures across diverse representations. While deep learning has advanced this field, existing models ofte…
DecoderImage SegmentationMedical Image AnalysisMedical Image Segmentation+3Auto-nnU-Net: Towards Automated Medical Image Segmentation
Medical Image Segmentation (MIS) includes diverse tasks, from bone to organ segmentation, each with its own challenges in finding the best segmentation model. The state-of-the-art AutoML-related MIS-framework nnU-Net aut…
AutoMLComputational EfficiencyHyperparameter OptimizationImage Segmentation+5MOSAIC: A Multi-View 2.5D Organ Slice Selector with Cross-Attentional Reasoning for Anatomically-Aware CT Localization in Medical Organ Segmentation
Efficient and accurate multi-organ segmentation from abdominal CT volumes is a fundamental challenge in medical image analysis. Existing 3D segmentation approaches are computationally and memory intensive, often processi…
Medical Image AnalysisOrgan SegmentationSegmentationBoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes
Obtaining large-scale medical data, annotated or unannotated, is challenging due to stringent privacy regulations and data protection policies. In addition, annotating medical images requires that domain experts manually…
Image SegmentationMedical Image SegmentationOrgan SegmentationSegmentation+1RadSAM: Segmenting 3D radiological images with a 2D promptable model
Medical image segmentation is a crucial and time-consuming task in clinical care, where mask precision is extremely important. The Segment Anything Model (SAM) offers a promising approach, as it provides an interactive i…
Image SegmentationMedical Image SegmentationOrgan SegmentationSegmentation+2MediAug: Exploring Visual Augmentation in Medical Imaging
Data augmentation is essential in medical imaging for improving classification accuracy, lesion detection, and organ segmentation under limited data conditions. However, two significant challenges remain. First, a pronou…
Brain Tumor ClassificationData AugmentationLesion DetectionOrgan SegmentationAnatomy-constrained modelling of image-derived input functions in dynamic PET using multi-organ segmentation
Accurate kinetic analysis of [$^{18}$F]FDG distribution in dynamic positron emission tomography (PET) requires anatomically constrained modelling of image-derived input functions (IDIFs). Traditionally, IDIFs are obtaine…
AnatomyOrgan SegmentationTextDiffSeg: Text-guided Latent Diffusion Model for 3d Medical Images Segmentation
Diffusion Probabilistic Models (DPMs) have demonstrated significant potential in 3D medical image segmentation tasks. However, their high computational cost and inability to fully capture global 3D contextual information…
Image SegmentationLatent Diffusion Model for 3DMedical Image SegmentationOrgan Segmentation+2Benchmarking Multi-Organ Segmentation Tools for Multi-Parametric T1-weighted Abdominal MRI
The segmentation of multiple organs in multi-parametric MRI studies is critical for many applications in radiology, such as correlating imaging biomarkers with disease status (e.g., cirrhosis, diabetes). Recently, three …
BenchmarkingOrgan SegmentationSelfMedHPM: Self Pre-training With Hard Patches Mining Masked Autoencoders For Medical Image Segmentation
In recent years, deep learning methods such as convolutional neural network (CNN) and transformers have made significant progress in CT multi-organ segmentation. However, CT multi-organ segmentation methods based on mask…
Image SegmentationMedical Image SegmentationOrgan SegmentationSegmentation+1MO-CTranS: A unified multi-organ segmentation model learning from multiple heterogeneously labelled datasets
Multi-organ segmentation holds paramount significance in many clinical tasks. In practice, compared to large fully annotated datasets, multiple small datasets are often more accessible and organs are not labelled consist…
DecoderOrgan SegmentationImitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification
The rapid increase in the number of Computed Tomography (CT) scan examinations has created an urgent need for automated tools, such as organ segmentation, anomaly classification, and report generation, to assist radiolog…
Anomaly ClassificationComputed Tomography (CT)Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1PG-SAM: Prior-Guided SAM with Medical for Multi-organ Segmentation
Segment Anything Model (SAM) demonstrates powerful zero-shot capabilities; however, its accuracy and robustness significantly decrease when applied to medical image segmentation. Existing methods address this issue throu…
Image SegmentationMedical Image SegmentationOrgan SegmentationSemantic SegmentationOrgan-aware Multi-scale Medical Image Segmentation Using Text Prompt Engineering
Accurate segmentation is essential for effective treatment planning and disease monitoring. Existing medical image segmentation methods predominantly rely on uni-modal visual inputs, such as images or videos, requiring l…
BenchmarkingDescriptiveImage SegmentationMedical Image Segmentation+4Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging
Positron emission tomography (PET) image denoising, along with lesion and organ segmentation, are critical steps in PET-aided diagnosis. However, existing methods typically treat these tasks independently, overlooking in…
DenoisingImage DenoisingOrgan SegmentationDeep Learning-Based Automated Workflow for Accurate Segmentation and Measurement of Abdominal Organs in CT Scans
Background: Automated analysis of CT scans for abdominal organ measurement is crucial for improving diagnostic efficiency and reducing inter-observer variability. Manual segmentation and measurement of organs such as the…
AnatomyDiagnosticOrgan SegmentationSegmentationPartially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation
Unpaired Multi-Modal Learning (UMML) which leverages unpaired multi-modal data to boost model performance on each individual modality has attracted a lot of research interests in medical image analysis. However, existing…
Image SegmentationMedical Image AnalysisMedical Image SegmentationOrgan Segmentation+3