Glioma Segmentation with Cascaded UNet

Lachinov, Dmitrii; Vasiliev, Evgeny; Турлапов, Вадим

doi:10.1007/978-3-030-11726-9_17

Cited by 46 publications

(28 citation statements)

References 10 publications

(18 reference statements)

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“…We compare three models: UNET, HEDNet and HED-Net with cGAN. UNET, which was originally proposed for biomedical image segmentation [8], is a standard and widely used model [9,11,10]. Results show that our HEDNet+cGAN model improves over both HEDNet and UNET.…”

Section: Quantitative Resultsmentioning

confidence: 96%

Improving Lesion Segmentation for Diabetic Retinopathy Using Adversarial Learning

Xiao

Zou

Yang

et al. 2019

Lecture Notes in Computer Science

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Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults. DR lesions can be challenging to identify in fundus images, and automatic DR detection systems can offer strong clinical value. Of the publicly available labeled datasets for DR, the Indian Diabetic Retinopathy Image Dataset (IDRiD) presents retinal fundus images with pixel-level annotations of four distinct lesions: microaneurysms, hemorrhages, soft exudates and hard exudates. We utilize the HEDNet edge detector to solve a semantic segmentation task on this dataset, and then propose an end-to-end system for pixel-level segmentation of DR lesions by incorporating HEDNet into a Conditional Generative Adversarial Network (cGAN). We design a loss function that adds adversarial loss to segmentation loss. Our experiments show that the addition of the adversarial loss improves the lesion segmentation performance over the baseline.

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Section: Quantitative Resultsmentioning

confidence: 96%

Improving Lesion Segmentation for Diabetic Retinopathy Using Adversarial Learning

Xiao

Zou

Yang

et al. 2019

Lecture Notes in Computer Science

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“…These algorithms can be trained directly on the 3D volume. Among the solutions, it is worth considering the results presented in Reference [81] in which the authors have trained a U-net network on images from BRATS 2018 and they have demonstrated that the same network can be used for the identification of the whole tumor, the enhancing tumor and the tumor core with great accuracy-0.908%, 0.784%, 0.844 respectively, while the method proposed in this paper is specialized on the detection of the whole tumor. For an extensive and complete review of deep learning techniques for GBM imaging we suggest Reference [82].…”

Section: Discussionmentioning

confidence: 99%

Towards Personalized Diagnosis of Glioblastoma in Fluid-Attenuated Inversion Recovery (FLAIR) by Topological Interpretable Machine Learning

2020

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Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumor, which tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumors. Usually, GBMs are detected by magnetic resonance images (MRI). Among MRI, a fluid-attenuated inversion recovery (FLAIR) sequence produces high quality digital tumor representation. Fast computer-aided detection and segmentation techniques are needed for overcoming subjective medical doctors (MDs) judgment. This study has three main novelties for demonstrating the role of topological features as new set of radiomics features which can be used as pillars of a personalized diagnostic systems of GBM analysis from FLAIR. For the first time topological data analysis is used for analyzing GBM from three complementary perspectives—tumor growth at cell level, temporal evolution of GBM in follow-up period and eventually GBM detection. The second novelty is represented by the definition of a new Shannon-like topological entropy, the so-called Generator Entropy. The third novelty is the combination of topological and textural features for training automatic interpretable machine learning. These novelties are demonstrated by three numerical experiments. Topological Data Analysis of a simplified 2D tumor growth mathematical model had allowed to understand the bio-chemical conditions that facilitate tumor growth—the higher the concentration of chemical nutrients the more virulent the process. Topological data analysis was used for evaluating GBM temporal progression on FLAIR recorded within 90 days following treatment completion and at progression. The experiment had confirmed that persistent entropy is a viable statistics for monitoring GBM evolution during the follow-up period. In the third experiment we developed a novel methodology based on topological and textural features and automatic interpretable machine learning for automatic GBM classification on FLAIR. The algorithm reached a classification accuracy up to 97%.

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“…In [153], the cascaded network was proposed, which initially segmented the whole tumor followed by tumor core, and enhancing tumor segmentation refinement. In [79] authors had proposed cascaded UNet with three networks. The network processed downsampled input and generated output was passed to the next network in the cascaded sequence.…”

Section: Cnn Methods Classification For Tumor Segmentationmentioning

confidence: 99%

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

Agravat¹,

Raval²

2021

Preprint

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<div>Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results.</div><div>The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates state-of-the-art methods. The complexity of tasks under the challenge has grown from segmentation (Task1) to overall survival prediction (Task 2) to uncertainty prediction for classification (Task 3). The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1. The aim is to showcase a complete change of trends in automated brain tumor models. The paper also covers end to end joint models involving brain tumor segmentation and overall survival prediction. All the methods are probed, and parameters that affect performance are tabulated and analyzed.</div>

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Glioma Segmentation with Cascaded UNet

Cited by 46 publications

References 10 publications

Improving Lesion Segmentation for Diabetic Retinopathy Using Adversarial Learning

Improving Lesion Segmentation for Diabetic Retinopathy Using Adversarial Learning

Towards Personalized Diagnosis of Glioblastoma in Fluid-Attenuated Inversion Recovery (FLAIR) by Topological Interpretable Machine Learning

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

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