# Tumor Segmentation

102 papers with code • 1 benchmarks • 6 datasets

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# Optimized U-Net for Brain Tumor Segmentation

7 Oct 2021

We propose an optimized U-Net architecture for a brain \mbox{tumor} segmentation task in the BraTS21 Challenge.

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# nnU-Net for Brain Tumor Segmentation

2 Nov 2020

We apply nnU-Net to the segmentation task of the BraTS 2020 challenge.

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# An attempt at beating the 3D U-Net

6 Aug 2019

The U-Net is arguably the most successful segmentation architecture in the medical domain.

2,188

# 3D MRI brain tumor segmentation using autoencoder regularization

27 Oct 2018

Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease.

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# Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

9 Jun 2019

Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.

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# H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes

21 Sep 2017

Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

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# Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)

6 Dec 2020

The proposed network achieved a $\mathrm{DSC}$ value of $0. 79 \pm 0. 20$, a mean surface distance of $5. 4 \pm 20. 2mm$ and $95\%$ Hausdorff distance of $14. 7 \pm 25. 0mm$ for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone.

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# The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge

2 Dec 2019

The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem.

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# The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

31 Mar 2019

The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment.

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# Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

20 Feb 2017

In the first step, we train a FCN to segment the liver as ROI input for a second FCN.

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