A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning

Rundo, Leonardo; Stefano, Alessandro; Militello, Carmelo; Russo, Giorgio; Sabini, M.G.; D’Arrigo, Corrado; Marletta, F; Ippolito, Massimo; Mauri, Giancarlo; Vitabile, Salvatore; Gilardi, Maria Carla

doi:10.1016/j.cmpb.2017.03.011

Cited by 48 publications

(42 citation statements)

References 71 publications

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“…In a complementary way, positron emission tomography (PET) image analysis quantitatively assesses the metabolic state of cancer. As we showed in Rundo et al (2017), it is not always possible to identify the actual tumor extent using the MRI modality alone. As a matter of fact, MRI and C‐Methionine‐Positron Emission Tomography (MET‐PET) convey different but highly correlated imaging information.…”

Section: Discussionmentioning

confidence: 78%

NeXt for neuro‐radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

Rundo

Militello

Tangherloni

et al. 2017

Int J Imaging Syst Tech

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Stereotactic neuro‐radiosurgery is a well‐established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft‐tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and growth. Therefore, particular attention must be deserved to these hypoxic areas that could lead to recurrent cancers and resistance to therapeutic damage. This article proposes a novel fully automatic method for necrosis extraction (NeXt), using the Fuzzy C‐Means algorithm, after the GTV segmentation. This unsupervised Machine Learning technique detects and delineates the necrotic regions also in heterogeneous cancers. The overall processing pipeline is an integrated two‐stage segmentation approach useful to support neuro‐radiosurgery. NeXt can be exploited for dose escalation, allowing for a more selective strategy to increase radiation dose in hypoxic radioresistant areas. Moreover, NeXt analyzes contrast‐enhanced T1‐weighted MR images alone and does not require multispectral MRI data, representing a clinically feasible solution. This study considers an MRI dataset composed of 32 brain metastatic cancers, wherein 20 tumors present necroses. The segmentation accuracy of NeXt was evaluated using both spatial overlap‐based and distance‐based metrics, achieving these average values: Dice similarity coefficient 95.93% ± 4.23% and mean absolute distance 0.225 ± 0.229 (pixels).

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Section: Discussionmentioning

confidence: 78%

NeXt for neuro‐radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

Rundo

Militello

Tangherloni

et al. 2017

Int J Imaging Syst Tech

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“…Novel nuclear medicine tracers for Positron Emission Tomography (PET) [76] and hyperpolarized carbon-13 ( 13 C) and sodium ( 23 Na) MRI [77] can considerably improve the specificity for evaluating PCa with respect to conventional imaging, by understanding the pyruvate conversion to lactate for estimating the cancer grade [78]. From a computational perspective, novel solutions must be devised to combine multi-modal imaging data [79]. In the case of DNNs, a topology explicitly designed for information exchange-between sub-networks processing the data from a single modality-through cross-connections, such as in the case of cross-modal CNNs (X-CNNs) [80], might be suitable for combining multi-modal imaging data.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI

et al. 2020

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Prostate Cancer (PCa) is the most common oncological disease in Western men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN.

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“…Furthermore, the proposed image processing pipeline facilitated segmentation of the entire organ despite the nonuniform signals within kidneys and heart. This work might potentially extend to segment tumor volumes in other diseases or alternative PET radiotracer studies 46 . The classifier has been made available to public at https://goo.gl/LzJyjY with pseudocodes shown in Supplementary Fig.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Features Differentiate Between Normal and Tumoral High-Fdg Uptake

Hsu

Doubrovin

Hua

et al. 2018

Sci Rep

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Identification of FDGavid- neoplasms may be obscured by high-uptake normal tissues, thus limiting inferences about the natural history of disease. We introduce a FDG-PET radiomics tissue classifier for differentiating FDGavid- normal tissues from tumor. Thirty-three scans from 15 patients with Hodgkin lymphoma and 68 scans from 23 patients with Ewing sarcoma treated on two prospective clinical trials were retrospectively analyzed. Disease volumes were manually segmented on FDG-PET and CT scans. Brain, heart, kidneys and bladder and tumor volumes were automatically segmented on PET images. Standard-uptake-value (SUV) derived shape and first order radiomics features were computed to build a random forest classifier. Manually segmented volumes were compared to automatically segmented tumor volumes. Classifier accuracy for normal tissues was 90%. Classifier performance was varied across normal tissue types (brain, left kidney and bladder, hear and right kidney were 100%, 96%, 97%, 83% and 87% respectively). Automatically segmented tumor volumes showed high concordance with the manually segmented tumor volumes (R2 = 0.97). Inclusion of texture-based radiomics features minimally contributed to classifier performance. Accurate normal tissue segmentation and classification facilitates accurate identification of FDGavid tissues and classification of those tissues as either tumor or normal tissue.

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A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning

Cited by 48 publications

References 71 publications

NeXt for neuro‐radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

NeXt for neuro‐radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI

Radiomics Features Differentiate Between Normal and Tumoral High-Fdg Uptake

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