Brain Tumor Cell Density Estimation from Multi-modal MR Images Based on a Synthetic Tumor Growth Model

Geremia, Ezequiel; Menze, Bjoern; Prastawa, Marcel; Weber, Marc‐André; Criminisi, Antonio; Ayache, Nicholas

doi:10.1007/978-3-642-36620-8_27

Cited by 8 publications

(9 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They claimed that context-aware features eliminate the need for a post-processing step imposing smoothness constraints by spatial regularization. Geremia et al (2012) had the idea to generate synthetic tumor images, which can be used to train a discriminative regression forest algorithm using different groups of features. They argued that this approach allowed them not only to segment patient images, but also to estimate latent tumor cell densities.…”

Section: Classification and Clusteringmentioning

confidence: 99%

A survey of MRI-based medical image analysis for brain tumor studies

et al. 2013

View full text Add to dashboard Cite

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

show abstract

Section: Classification and Clusteringmentioning

confidence: 99%

A survey of MRI-based medical image analysis for brain tumor studies

et al. 2013

View full text Add to dashboard Cite

show abstract

“…This last approach is applicable to a wide variety of cases, but also bears some limitations: i) the complex distribution of image intensities for tumor compartments is summarized by its expectation, which is oblivious of multi-modal intensity distributions, ii) the inter-patient MR normalization procedure is not specified, which makes it difficult to standardize real MRI so that they look like synthetic MRI, typically for the training of machine learning algorithms [29], iii) simulated images do not show the variability of intensity of realistic MR scans, and the addition of a very high Gaussian noise only limits this effect.…”

Section: B Related Workmentioning

confidence: 99%

“…The simulator of synthetic pathological MRI 1 described in [28] has been used in a number of research articles mostly for prototyping and validation, in the context of glioma segmentation [3], [29], [30], outlier detection algorithm [31], registration of a healthy brain atlas to a tumor-bearing patient image [32], and construction of a brain atlas [33]. Other applications include the training of machine-learning algorithms for glioma segmentation.…”

Section: B Related Workmentioning

confidence: 99%

“…Other applications include the training of machine-learning algorithms for glioma segmentation. [29] trained random forest on a large dataset consisting of 740 synthetic cases and showed good segmentation results on a few real cases for testing. [31] performed a thorough validation of an outlier detection algorithm, based on 100 MR scans synthesized with different tumor volumes to test the robustness of the algorithm to the amount of outliers.…”

Section: B Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Extended Modality Propagation: Image Synthesis of Pathological Cases

Cordier

Delingette

et al. 2016

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

This paper describes a novel generative model for the synthesis of multi-modal medical images of pathological cases based on a single label map. Our model builds upon i) a generative model commonly used for label fusion and multi-atlas patch-based segmentation of healthy anatomical structures, ii) the Modality Propagation iterative strategy used for a spatially-coherent synthesis of subject-specific scans of desired image modalities. The expression Extended Modality Propagation is coined to refer to the extension of Modality Propagation to the synthesis of images of pathological cases. Moreover, image synthesis uncertainty is estimated. An application to Magnetic Resonance Imaging synthesis of glioma-bearing brains is i) validated on the training dataset of a Multimodal Brain Tumor Image Segmentation challenge, ii) compared to the state-of-the-art in glioma image synthesis, and iii) illustrated using the output of two different tumor growth models. Such a generative model allows the generation of a large dataset of synthetic cases, which could prove useful for the training, validation, or benchmarking of image processing algorithms.

show abstract

“…Bauer et al [24] adopt a hybrid method, based on SVM and hierarchical regularisation, to segment tumour and healthy tissues, including sub-compartments. Random Forest-based methods are proposed by Zikic et al [25] to identify brain tumour sub-compartments from multi-modal images and by Geremia et al [26] that generate synthetic tumour images to train a discriminative regression forest algorithm using different groups of features.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Behaviour of Machine Learning Techniques to Segment Brain Metastases in Radiation Therapy Planning

et al. 2019

View full text Add to dashboard Cite

This work aimed to investigate whether automated classifiers belonging to feature-based and deep learning may approach brain metastases segmentation successfully. Support Vector Machine and V-Net Convolutional Neural Network are selected as representatives of the two approaches. In the experiments, we consider several configurations of the two methods to segment brain metastases on contrast-enhanced T1-weighted magnetic resonance images. Performances were evaluated and compared under critical conditions imposed by the clinical radiotherapy domain, using in-house dataset and public dataset created for the Multimodal Brain Tumour Image Segmentation (BraTS) challenge. Our results showed that the feature-based and the deep network approaches are promising for the segmentation of Magnetic Resonance Imaging (MRI) brain metastases achieving both an acceptable level of performance. Experimental results also highlight different behaviour between the two methods. Support vector machine (SVM) improves performance with a smaller training set, but it is unable to manage a high level of heterogeneity in the data and requires post-processing refinement stages. The V-Net model shows good performances when trained on multiple heterogeneous cases but requires data augmentations and transfer learning procedures to optimise its behaviour. The paper illustrates a software package implementing an integrated set of procedures for active support in segmenting brain metastases within the radiotherapy workflow.

show abstract

Brain Tumor Cell Density Estimation from Multi-modal MR Images Based on a Synthetic Tumor Growth Model

Cited by 8 publications

References 25 publications

A survey of MRI-based medical image analysis for brain tumor studies

A survey of MRI-based medical image analysis for brain tumor studies

Extended Modality Propagation: Image Synthesis of Pathological Cases

Investigating the Behaviour of Machine Learning Techniques to Segment Brain Metastases in Radiation Therapy Planning

Contact Info

Product

Resources

About