Segmentation of Head and Neck Tumours Using Modified U-net

Zhao, Baixiang; Soraghan, John J.; Caterina, Gaetano Di; Grose, Derek

doi:10.23919/eusipco.2019.8902637

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…Cancer is a leading cause of death worldwide, and MR is one of the strongest methods for the proper prognosis of different types of cancers. In addition to brain cancer, we have found applications on prostate cancer [58], [64], [152]- [156], liver cancer [21], [157], [158], nasopharyngeal cancer [25], [98], [159], and breast cancer [99], [160]. Other implementations include segmentation of the femur [12]- [14], spinal cord [161], [162], blood vessels [100], vertebral column [17], human placenta [163], and the uterus [164].…”

Section: A Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: A Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…Another recent relevant work was Mask-RCNN, a novel UNet structure introduced by Vuola et al (2019) through combining UNet and Mask-RCNN for nucleus segmentation. A U-shaped structure with dilated convolution path named Modified UNet was implemented by Zhao et al (2019) . UNet + + was employed to alleviate the a priori unknown of depth by Zhou et al (2020) , and the restrictive fusion architecture of UNet skip connections was also optimized in it.…”

Section: Introductionmentioning

confidence: 99%

A Novel Elastomeric UNet for Medical Image Segmentation

Cai

Chen

2022

Front. Aging Neurosci.

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Medical image segmentation is of important support for clinical medical applications. As most of the current medical image segmentation models are limited in the U-shaped structure, to some extent the deep convolutional neural network (CNN) structure design is hard to be accomplished. The design in this study mimics the way the wave is elastomeric propagating, extending the structure from both the horizontal and spatial dimensions for realizing the Elastomeric UNet (EUNet) structure. The EUNet can be divided into two types: horizontal EUNet and spatial EUNet, based on the propagation direction. The advantages of this design are threefold. First, the training structure can be deepened effectively. Second, the independence brought by each branch (a U-shaped design) makes the flexible design redundancy available. Finally, a horizontal and vertical series-parallel structure helps on feature accumulation and recursion. Researchers can adjust the design according to the requirements to achieve better segmentation performance for the independent structural design. The proposed networks were evaluated on two datasets: a self-built dataset (multi-photon microscopy, MPM) and publicly benchmark retinal datasets (DRIVE). The results of experiments demonstrated that the performance of EUNet outperformed the UNet and its variants.

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“…Some researchers have exclusively considered the morphological modalities, i.e. CT [10][11][12][13][14][15] and MRI [16][17][18][19][20] or a combination of both [21] with Dice similarity coefficients (DSCs) reaching 0.74 for primary tumor and 0.66 for LN metastases in CT and 0.65 for primary tumor and 0.58 for LN metastases in MRI. For the special case of MRI in nasopharyngeal cancer a much higher DSC of up to 0.90 has been reported [19].…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in [18F]FDG PET/CT

Nikulin

Zschaeck

Maus

et al. 2022

Preprint

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Purpose: PET-derived metabolic tumor volume (MTV) and total lesion glycolysis of the primary tumor are known to be prognostic of clinical outcome in head and neck cancer (HNC). Including evaluation of lymph node metastases can further increase the prognostic value of PET but accurate manual delineation and classification of all lesions is time-consuming and prone to inter-observer variability. Our goal, therefore, was development and evaluation of an automated tool for MTV delineation/classification of primary tumor and lymph node metastases in PET/CT investigations of HNC patients. Methods: Automated lesion delineation was performed with a residual 3D U-Net convolutional neural network (CNN) incorporating a multi-head self-attention block. 698 [18F]FDG PET/CT scans from 3 different sites and 5 public databases were used for network training and testing. An external dataset of 181 [18F]FDG PET/CT scans from 2 additional sites was employed to assess the generalizability of the network. In these data, primary tumor and metastases were interactively delineated and labeled by two experienced physicians. Performance of the trained network models was assessed by 5-fold cross-validation in the main dataset and by pooling results from the 5 developed models in the external dataset. The Dice similarity coefficient (DSC) for individual delineation tasks and the primary tumor/metastasis classification accuracy were used as evaluation metrics. Additionally, a survival analysis using univariate Cox regression was performed comparing achieved group separation for manual and automated delineation, respectively. Results: In the cross-validation experiment, delineation of all malignant lesions with the trained U-Net models achieves DSC of 0.885, 0.805, and 0.870 for primary tumor, LN metastases, and the union of both, respectively. In external testing, the DSC reaches 0.850, 0.724, and 0.823 for primary tumor, LN metastases, and the union of both, respectively. The voxel classification accuracy was 98.0% and 97.9% in cross-validation and external data, respectively. Univariate Cox analysis in the cross-validation and the external testing reveals that manually and automatically derived total MTVs are both highly prognostic with respect to overall survival, yielding essentially identical hazard ratios (HR) (HRman = 1.9; p < 0.001 vs. HRcnn = 1.8; p < 0.001 in cross-validation and HRman = 1.8; p = 0.011 vs. HRcnn = 1.9; p = 0.004 in external testing). Conclusion: To the best of our knowledge, this work presents the first CNN model for successful MTV delineation and lesion classification in HNC. In the vast majority of patients, the network performs satisfactory delineation and classification of primary tumor and lymph node metastases and only rarely requires more than minimal manual correction. It is thus able to massively facilitate study data evaluation in large patient groups and also does have clear potential for supervised clinical application.

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Segmentation of Head and Neck Tumours Using Modified U-net

Cited by 10 publications

References 16 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

A Novel Elastomeric UNet for Medical Image Segmentation

A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in [18F]FDG PET/CT

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