Deep Learning in Multi-organ Segmentation
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications. These methods were classified into six categories according to their network design. For each category, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review of each category, we briefly discussed its achievements, shortcomings and future potentials. We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge datasets and 2015 MICCAI Head Neck Auto-Segmentation Challenge datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningImage SegmentationMedical Image SegmentationOrgan SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble
Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis. Recently, the immense success of deep learning motivated its wide adoption in …
Medical Image AnalysisOrgan SegmentationSegmentationBoundary-Aware Network for Abdominal Multi-Organ Segmentation
Automated abdominal multi-organ segmentation is a crucial yet challenging task in the computer-aided diagnosis of abdominal organ-related diseases. Although numerous deep learning models have achieved remarkable success …
DecoderImage SegmentationMedical Image SegmentationOrgan Segmentation+2SelfMedHPM: 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+1HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery
The wide range of research in deep learning-based medical image segmentation pushed the boundaries in a multitude of applications. A clinically relevant problem that received less attention is the handling of scans with …
AnatomyDeep LearningHallucinationImage Segmentation+4Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation
The task of labeling multiple organs for segmentation is a complex and time-consuming process, resulting in a scarcity of comprehensively labeled multi-organ datasets while the emergence of numerous partially labeled dat…
Organ SegmentationPartially Labeled DatasetsSegmentation