TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors

Lee, Matthew Chung Hai; Petersen, Kathrine Birch; Pawlowski, Nick; Glocker, Ben; Schaap, Michiel

doi:10.1109/tmi.2019.2905990

Cited by 104 publications

(51 citation statements)

References 43 publications

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“…In particular, with respect to challenges such as generalisability, explainability and data efficiency it is likely that the combination of traditional model-and data-driven approaches will lead to future advances. Several approaches have already demonstrated that priors in form of anatomical models can significantly improve the performance of applications such as segmentation [67,21] or super-resolution reconstruction [84].…”

Section: Discussionmentioning

confidence: 99%

Model-Based and Data-Driven Strategies in Medical Image Computing

Rueckert

Schnabel

2020

Proc. IEEE

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Model-based approaches for image reconstruction, analysis and interpretation have made significant progress over the last decades. Many of these approaches are based on either mathematical, physical or biological models. A challenge for these approaches is the modelling of the underlying processes (e.g. the physics of image acquisition or the patho-physiology of a disease) with appropriate levels of detail and realism. With the availability of large amounts of imaging data and machine learning (in particular deep learning) techniques, data-driven approaches have become more widespread for use in different tasks in reconstruction, analysis and interpretation. These approaches learn statistical models directly from labelled or unlabeled image data and have been shown to be very powerful for extracting clinically useful information from medical imaging. While these data-driven approaches often outperform traditional model-based approaches, their clinical deployment often poses challenges in terms of robustness, generalization ability and interpretability. In this article, we discuss what developments have motivated the shift from model-based approaches towards data-driven strategies and what potential problems are associated with the move towards purely data-driven approaches, in particular deep learning. We also discuss some of the open challenges for data-driven approaches, e.g. generalization to new unseen data (e.g. transfer learning), robustness to adversarial attacks and interpretability. Finally, we conclude with a discussion on how these approaches may lead to the development of more closely coupled imaging pipelines that are optimized in an end-to-end fashion.All steps of the medical imaging pipeline typically make extensive use of models. In this paper we define the term model in a very general fashion, in the sense that it provides a transformation of input (data) into the desired output. For example, in image reconstruction the model helps to transform the acquired sensor data into an image, in image registration the model is used to relate two images (inputs) via a transformation (output), and in image segmentation the model is used to transform an image in which each pixel corresponds to an intensity value, into an semantic segmentation where each pixel has a label corresponding to an organ, anatomical structure or pathology. In diagnosis, the model helps to map image derived features and biomarkers

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Section: Discussionmentioning

confidence: 99%

Model-Based and Data-Driven Strategies in Medical Image Computing

Rueckert

Schnabel

2020

Proc. IEEE

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“…Nevertheless, it is beneficial and important to incorporate shape priors of the segmentation object with the segmentation models for improved performance and accelerated convergence. A strong reason is that shape is one of the most important geometric attributes of anatomical objects [21] , and the shape priors can reduce the searching space of the potential segmentation outputs for deep learning models [22] . In addition, the shape mesh provides sufficient information for the identification of 3D objects [23] .…”

Section: Related Workmentioning

confidence: 99%

Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images

Zhao

et al. 2021

Pattern Recognition

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This paper aims to develop an automatic method to segment pulmonary parenchyma in chest CT images and analyze texture features from the segmented pulmonary parenchyma regions to assist radiologists in COVID-19 diagnosis. A new segmentation method, which integrates a three-dimensional (3D) V-Net with a shape deformation module implemented using a spatial transform network (STN), was proposed to segment pulmonary parenchyma in chest CT images. The 3D V-Net was adopted to perform an end-to-end lung extraction while the deformation module was utilized to restrict the V-Net output according to the prior shape knowledge. The proposed segmentation method was validated against the manual annotation generated by experienced operators. The radiomic features measured from our segmentation results were further analyzed by sophisticated statistical models with high interpretability to discover significant independent features and detect COVID-19 infection. Experimental results demonstrated that compared with the manual annotation, the proposed segmentation method achieved a Dice similarity coefficient of 0.9796, a sensitivity of 0.9840, a specificity of 0.9954, and a mean surface distance error of 0.0318 mm. Furthermore, our COVID-19 classification model achieved an area under curve (AUC) of 0.9470, a sensitivity of 0.9500, and a specificity of 0.9270 when discriminating lung infection with COVID-19 from community-acquired pneumonia and healthy controls using statistically significant radiomic features. The significant features measured from our segmentation results agreed well with those from the manual annotation. Our approach has great promise for clinical use in facilitating automatic diagnosis of COVID-19 infection on chest CT images.

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“…Huang et al (44) used centerlines to obtain a reformatted image in which the lumen was segmented using a 3D CNN. Lee et al (42) use centerlines to obtain a tube-shaped prior that is deformed to segment the coronary lumen. Lumen segmentation is often considered a preprocessing step for stenosis detection, but it has been shown that stenosis degree can also be directly determined based on image data.…”

Section: Anatomical Significancementioning

confidence: 99%

Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey

Hampe

Wolterink

Velzen

et al. 2019

Front. Cardiovasc. Med.

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Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.

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TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors

Cited by 104 publications

References 43 publications

Model-Based and Data-Driven Strategies in Medical Image Computing

Model-Based and Data-Driven Strategies in Medical Image Computing

Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images

Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey

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