Deep learning for variational multimodality tumor segmentation in PET/CT

Li, Laquan; Zhao, Xiangming; Lu, Wei; Tan, Shan

doi:10.1016/j.neucom.2018.10.099

Cited by 115 publications

(68 citation statements)

References 45 publications

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“…A network having both inputs can learn the physiological uptake of different organs and enhance the segmentation (e.g., improved definition of (metastatic) tumor nodules). First approaches that use both anatomical and molecular images to enhance segmentation have been undertaken with PET/CT in automatic tumor segmentation in lung cancer [45][46][47], head and neck tumors [48,49], and non-Hodgkin lymphoma [50]. Here, a combination of the tumor detection task and the multi-organ segmentation can be advantageous Fig.…”

Section: Artificial Intelligence For Segmentationmentioning

confidence: 99%

How molecular imaging will enable robotic precision surgery

Wendler

Leeuwen

Navab

et al. 2021

Eur J Nucl Med Mol Imaging

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Molecular imaging is one of the pillars of precision surgery. Its applications range from early diagnostics to therapy planning, execution, and the accurate assessment of outcomes. In particular, molecular imaging solutions are in high demand in minimally invasive surgical strategies, such as the substantially increasing field of robotic surgery. This review aims at connecting the molecular imaging and nuclear medicine community to the rapidly expanding armory of surgical medical devices. Such devices entail technologies ranging from artificial intelligence and computer-aided visualization technologies (software) to innovative molecular imaging modalities and surgical navigation (hardware). We discuss technologies based on their role at different steps of the surgical workflow, i.e., from surgical decision and planning, over to target localization and excision guidance, all the way to (back table) surgical verification. This provides a glimpse of how innovations from the technology fields can realize an exciting future for the molecular imaging and surgery communities.

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Section: Artificial Intelligence For Segmentationmentioning

confidence: 99%

How molecular imaging will enable robotic precision surgery

Wendler

Leeuwen

Navab

et al. 2021

Eur J Nucl Med Mol Imaging

View full text Add to dashboard Cite

show abstract

“…3) Data Augmentation: CNNs demonstrated state-of-theart performance in different tasks [25]- [27]. However, the performance of CNNs highly depends on training data size.…”

Section: B Experimental Datasetmentioning

confidence: 99%

A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification

Kassani

Wesolowski

et al. 2019

2019 International Conference on Information and Communication Technology Convergence (ICTC)

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Automatic detection of leukemic B-lymphoblast cancer in microscopic images is very challenging due to the complicated nature of histopathological structures. To tackle this issue, an automatic and robust diagnostic system is required for early detection and treatment. In this paper, an automated deep learning-based method is proposed to distinguish between immature leukemic blasts and normal cells. The proposed deep learning based hybrid method, which is enriched by different data augmentation techniques, is able to extract high-level features from input images. Results demonstrate that the proposed model yields better prediction than individual models for Leukemic Blymphoblast classification with 96.17% overall accuracy, 95.17% sensitivity and 98.58% specificity. Fusing the features extracted from intermediate layers, our approach has the potential to improve the overall classification performance.

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“…Cross-domain image synthesis 2 has been used to impute incomplete information in standard statistical analysis [1] , [2] , to predict and simulate developments of missing information [3] , or to improve intermediate steps of analysis such as registration [4] , information fusion [5] , [6] , [7] , segmentation [8] , [9] , [10] , atlas construction [11] , [12] and disease classification [13] , [14] . These methods map between MRI, computed tomography (CT), positron emission tomography (PET) and ultrasound imaging from one domain to another.…”

Section: Introductionmentioning

confidence: 99%

DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis

et al. 2021

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Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.

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Deep learning for variational multimodality tumor segmentation in PET/CT

Cited by 115 publications

References 45 publications

How molecular imaging will enable robotic precision surgery

How molecular imaging will enable robotic precision surgery

A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification

DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis

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