Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

By Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, Paul J. Kennedy. Journal of digital imaging, 2019

1,522 citations recorded by the source corpus.

Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.

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

View Paper: https://www.semanticscholar.org/paper/40f56e1c57bdefb22a8b4a635ea40f7d37c72d27

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation (2021, background): “Convolutional neural networks (CNNs) have attained unparalleled performance in numerous medical image segmentation tasks [9,12], such as multi-organ segmentation, liver lesion segmentation, brain 3D MRI, etc., as it is proved to be powerful at building hierarchical task-speciﬁc feature…”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, methodology): “An overview of various DL models for segmentation of medical images is presented in [53].”
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation (2019, methodology): “Huang et al. (2018b) use their method for colorectal tumor segmentation in MRI volumes.”
Segmenter: Transformer for Semantic Segmentation (2021, background): “Semantic segmentation is a challenging computer vision problem with a wide range of applications including autonomous driving, robotics, augmented reality, image editing, medical imaging and many others [27, 28, 45].”
Artificial intelligence and machine learning for medical imaging: A technology review. (2021, neutral): “Classification can be binary, like in determining whether a pathology is present or not in an image [25,27], involve multiple classes, as in determining a particular pathology among several labels [28 – 30], or concern not the whole image but each pixel, as done for image segmentation [31,32].”
Segment Anything Model for medical image analysis: an experimental study (2023, background): “Deep learning has dramatically altered how task is performed and brought significant performance improvements in many domains [6, 15].”
CaraNet: context axial reverse attention network for segmentation of small medical objects (2021, background): “And it is very common in medical image segmentation that the anatomy of interest only occupies a very small portion of the image [17].”
A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis (2019, neutral): “We refer the reader to a number of reviews of DL in medical imaging (Hesamian et al., 2019; Lundervold and Lundervold, 2019; Yamashita et al., 2018).”
Mutual consistency learning for semi-supervised medical image segmentation (2021, background): “At the same time, with an effective segmentation model, the localization of particular objects is significant for the early screening and precise assessment of relevant diseases (Hesamian et al., 2019).”
Channel prior convolutional attention for medical image segmentation (2023, background): “A vital role in medical image segmentation is played by deep learning methods (Hesamian et al., 2019).”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “There are many survey papers on deep learning based key technologies for medical image analysis [9], [10], [11], [12], [13], [14], [15], [16], [17], [18].”
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment (2020, background): “network is significantly capable of nonlinear modeling and has extensive use in medical image processing and diagnosis process [25]–[28].”
Medical Image Segmentation Using Deep Learning: A Survey (2020, background): “ncluding two major dataset limitations: scarce annotations and weak annotations. All these surveys play an important role for the development of medical image segmentation techniques. Hesamian et al. [25]reviewed on three aspects of approaches (network structures), training techniques, and challenge”