Medical Image Segmentation Using Deep Learning: A Survey

Abstract

Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. In this paper, we present a comprehensive thematic survey on medical image segmentation using deep learning techniques. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi-level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyze literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.

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Cited by in corpus (17)

• CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation
• BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once
• MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation
• GLIMS: Attention-Guided Lightweight Multi-Scale Hybrid Network for Volumetric Semantic Segmentation
• HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation
• Defining the boundaries: challenges and advances in identifying cells in microscopy images
• Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning
• Neural Network Methods for Radiation Detectors and Imaging
• Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning Network for Semi-supervised 3D Medical Image Segmentation
• Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applications
• Fully automated workflow for designing patient-specific orthopaedic implants: application to total knee arthroplasty
• BC-MRI-SEG: A Breast Cancer MRI Tumor Segmentation Benchmark
• Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain
• DualSwinUnet++: An Enhanced Swin-Unet Architecture With Dual Decoders For PTMC Segmentation
• GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation
• Meta-learners for few-shot weakly-supervised optic disc and cup segmentation on fundus images
• ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentation