A framework based on hidden Markov trees for multimodal <scp>PET</scp>/<scp>CT</scp> image co‐segmentation

Salah, Houda Hanzouli-Ben; Lapuyade‐Lahorgue, Jérôme; Bert, Julien; Benoit, Didier; Lambin, Philippe; Baardwijk, Angela van; Monfrini, Emmanuel; Pieczynski, Wojciech; Visvikis, Dimitris; Hatt, Mathieu

doi:10.1002/mp.12531

Cited by 11 publications

(13 citation statements)

References 62 publications

(120 reference statements)

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“…Accurate tumor delineation in image‐guided radiotherapy, is critically important yet efforts to automate the process for radiotherapy treatment planning or delivery remain elusive . In this work, our DFCN‐CoSeg approach for PET‐CT offers improved automation, requires no direct interaction and enables efficient computer‐aided segmentation which may facilitate eventual clinical use.…”

Section: Discussionmentioning

confidence: 99%

“…Accurate tumor delineation in image-guided radiotherapy, is critically important yet efforts to automate the process for radiotherapy treatment planning or delivery remain elusive. 19 In this work, our DFCN-CoSeg approach for PET-CT offers improved automation, requires no direct interaction and enables efficient computer-aided segmentation which may facilitate eventual clinical use. Our framework takes advantage of the assumption that the combination of information derived from dual image modality (PET-CT) would vastly improve the capability of an automated, learning-based segmentation approach.…”

Section: A Performancementioning

confidence: 99%

“…11,12 Substantial progress has been made in automating the tumor definition extracted from PET-CT scans. [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] Although these methods showed promise, there are still limitations when adapting them for clinical use. Most previous methods depend on user-defined foreground seeds belonging to the tumor.…”

Section: Introductionmentioning

confidence: 99%

“…To take advantage of the dual modality nature of PET‐CT imaging, cosegmentation aims to simultaneously compute the tumor volume defined on the CT image as well as that defined on the PET image by combining physiological information from the PET image with the anatomical information from CT image . Substantial progress has been made in automating the tumor definition extracted from PET‐CT scans . Although these methods showed promise, there are still limitations when adapting them for clinical use.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

et al. 2019

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Purpose To investigate the use and efficiency of 3‐D deep learning, fully convolutional networks (DFCN) for simultaneous tumor cosegmentation on dual‐modality nonsmall cell lung cancer (NSCLC) and positron emission tomography (PET)‐computed tomography (CT) images. Methods We used DFCN cosegmentation for NSCLC tumors in PET‐CT images, considering both the CT and PET information. The proposed DFCN‐based cosegmentation method consists of two coupled three‐dimensional (3D)‐UNets with an encoder‐decoder architecture, which can communicate with the other in order to share complementary information between PET and CT. The weighted average sensitivity and positive predictive values denoted as Scores, dice similarity coefficients (DSCs), and the average symmetric surface distances were used to assess the performance of the proposed approach on 60 pairs of PET/CTs. A Simultaneous Truth and Performance Level Estimation Algorithm (STAPLE) of 3 expert physicians’ delineations were used as a reference. The proposed DFCN framework was compared to 3 graph‐based cosegmentation methods. Results Strong agreement was observed when using the STAPLE references for the proposed DFCN cosegmentation on the PET‐CT images. The average DSCs on CT and PET are 0.861 ± 0.037 and 0.828 ± 0.087, respectively, using DFCN, compared to 0.638 ± 0.165 and 0.643 ± 0.141, respectively, when using the graph‐based cosegmentation method. The proposed DFCN cosegmentation using both PET and CT also outperforms the deep learning method using either PET or CT alone. Conclusions The proposed DFCN cosegmentation is able to outperform existing graph‐based segmentation methods. The proposed DFCN cosegmentation shows promise for further integration with quantitative multimodality imaging tools in clinical trials.

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Section: Discussionmentioning

confidence: 99%

Section: A Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

et al. 2019

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“…The acronyms stand respectively for North West, North East, South East and South West.2 ∀s ∈ S, if Ys (the observation) does not exists, we take p(Ys|Xs) ∝ 1 3. The conditioning arguments respectively match x s − and v v v s − in Eq.…”

mentioning

confidence: 99%

Unsupervised Image Segmentation with Spatial Triplet Markov Trees

Gangloff

Courbot

Monfrini

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Hidden Markov Trees (HMTs) are successful probabilistic models [1] [2] [3] in image segmentation or genetic analysis for example. They offer a good compromise between the random variables that can be modeled and the tractability of the inference within these models. Indeed, the inference procedures can be conducted in a direct, exact fashion [4]. However the conditional independence restrictions in HMTs can be too simplistic with respect to the interactions we wish to model, particularly in the task of natural image segmentation. In this article we study an extension of HMTs called Spatial Triplet Markov Trees (STMTs) which is designed to greatly increase the correlations of the random variables while keeping the possibility of direct and exact inference procedures. The STMT model is tested and compared to other unsupervised segmentation techniques in the case of unsupervised image segmentation. Numerical experiments show that STMTs outperform HMTs and present rich spatial dependencies which are crucial for image segmentation.

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Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting

Iantsen

Ferreira

Lucia

et al. 2021

Eur J Nucl Med Mol Imaging

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Purpose In this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter images with heterogeneous characteristics. Methods In cervical cancer, an additional challenge is the location of the tumor uptake near or even stuck to the bladder. PET datasets of 232 patients from five institutions were exploited. To avoid unreliable manual delineations, the ground truth was generated with a semi-automated approach: a volume containing the tumor and excluding the bladder was first manually determined, then a well-validated, semi-automated approach relying on the Fuzzy locally Adaptive Bayesian (FLAB) algorithm was applied to generate the ground truth. Our model built on the U-Net architecture incorporates residual blocks with concurrent spatial squeeze and excitation modules, as well as learnable non-linear downsampling and upsampling blocks. Experiments relied on cross-validation (four institutions for training and validation, and the fifth for testing). Results The model achieved good Dice similarity coefficient (DSC) with little variability across institutions (0.80 ± 0.03), with higher recall (0.90 ± 0.05) than precision (0.75 ± 0.05) and improved results over the standard U-Net (DSC 0.77 ± 0.05, recall 0.87 ± 0.02, precision 0.74 ± 0.08). Both vastly outperformed a fixed threshold at 40% of SUVmax (DSC 0.33 ± 0.15, recall 0.52 ± 0.17, precision 0.30 ± 0.16). In all cases, the model could determine the tumor uptake without including the bladder. Neither shape priors nor anatomical information was required to achieve efficient training. Conclusion The proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context.

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A framework based on hidden Markov trees for multimodal PET/CT image co‐segmentation

Abstract: We evaluated the accuracy of the proposed HMT-based framework for PET/CT image segmentation. The proposed method reached good accuracy, especially with pre-processing in the contourlet domain.

Cited by 11 publications

References 62 publications

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

Unsupervised Image Segmentation with Spatial Triplet Markov Trees

Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting

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