Deep semantic segmentation of natural and medical images: a review

By Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen, Julien Cohen-Adad, Ghassan Hamarneh. Artificial Intelligence Review, 2019

826 citations recorded by the source corpus.

The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups. Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b0968cfeb5f3ae01da5bd04cdabd018fd0829966

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Citations in the corpus

Spatial components of molecular tissue biology (2022, neutral): “Their representation could be used for downstream tasks, such as integration with molecular data 105 .”
CaraNet: context axial reverse attention network for segmentation of small medical objects (2021, background): “It can effectively use information transferred from several subsequent feature maps to detect the salience features [19].”
Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation (2022, background): “In this process, the encoder–decoder structure exhibits excellent segmentation performance and has gradually become a popular structure conﬁguration in semantic segmentation networks [12], [13].”
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation (2019, background): “, Tajbakhsh et al. (2019a) and Ross et al.”
Medical Image Segmentation Using Deep Learning: A Survey (2020, background): “[22]discussed the development of semantic and medical image segmentation; they”
Medical Image Segmentation Review: The Success of U-Net (2022, background): “Image segmentation tasks can be classified into two categories: semantic segmentation and instance segmentation [2], [3].”
Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images (2020, background): “Recently, tremendous progress has been made in medical semantic segmentation [15] thanks to modern deep convolutional networks, which achieve state-of-the-art performances in many real-world tasks.”
PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers (2022, background): “With the increasing demand of intelligence, semantic segmentation has become the basic perception component for applications such as autonomous driving [16], medical imaging diagnosis [2] and remote sensing imagery [54].”