Tumor Segmentation

92 papers with code • 1 benchmarks • 5 datasets

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Greatest papers with code

nnU-Net for Brain Tumor Segmentation

MIC-DKFZ/nnunet 2 Nov 2020

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

Brain Tumor Segmentation Data Augmentation +1

An attempt at beating the 3D U-Net

MIC-DKFZ/nnunet 6 Aug 2019

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

Tumor Segmentation

3D MRI brain tumor segmentation using autoencoder regularization

black0017/MedicalZooPytorch 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.

Brain Tumor Segmentation Tumor Segmentation

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

xmengli999/H-DenseUNet 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.

Automatic Liver And Tumor Segmentation Lesion Segmentation +2

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

LIVIAETS/surface-loss 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.

Tumor Segmentation

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

neheller/kits19 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.

3D Semantic Segmentation Tumor Segmentation

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

neheller/kits19 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.

Computed Tomography (CT) Decision Making +1

Brain Tumor Segmentation with Deep Neural Networks

naldeborgh7575/brain_segmentation 13 May 2015

Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.

Brain Tumor Segmentation Tumor Segmentation