Posts Tagged ‘deep learning’
Complete List of Challenge Submissions
Kits19TumorSegmentation with VNet
Chen, Junqiang
we design the deep learning network Vnet for segmentation tumor and kidney.Fist, preprocess the kidney and kidney tumor data,second, segmentating kidney progress split into two steps: Corse segmentation and fine segmentation.third,segmentation kidney tumor process split into two steps: 2d segmentation and 3d segmentation
Kidney Tumour Segmentation
Sharma, Rochan
Medical Image Segmentation is a challenging field in the area of Computer Vision. In this work Two deep learning models were explored namely U-Net and ENet. The reason to shortlist U-Net was it is suitable on a small data set and also originally designed for Biomedical Image segmentation. However when compared to ENet it is much slower. To speed up the process of Kidney Tumor segmentation , ENet was shortlisted and also experimented on the data set provided. ENet was very fast as compared to U-Net , However some visual representations of the predicted results have shown promising results in U-Net better then ENet. A classification model called as Xception Model was also considered right in the first phase , so as to shortlist those slices from the CT which have the presence of Kidney. So that the output obtained can be given as an input to semantic segmentation model. This would allows us to speed up the process of Kidney Tumor Segmentation. Finally performance parameter which was used for evaluation segmentation models was IOU.
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
Müller, Dominik
Kramer, Frank
The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn. The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization. Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model. With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. Additionally, MIScnn was used to deploy a Residual 3D U-Net model for participating at the Kidney Tumor Segmentation Challenge 2019. The source code for MIScnn is available in the Git repository: https://github.com/frankkramerlab/MIScnn.
Development of a Deep Learning Algorithm for Segmentation of Kidney Tumor Imaging
Raein Hashemi, Seyed
Gershman, Boris
Valtchinov, Vladimir I.
Development of a Deep Learning Algorithm for Segmentation of Kidney Tumor Imaging
Kidney and tumor segmentation using combined Deep learning method
Kamkova, Yuliia
Ali Qadir, Hemin
Jakob, Ole
Prasanna Kumar, Rahul
This paper presents our method for automatic segmentation for kidney and tumor as part of the Kidney Tumor Segmentation Challenge (KiTS19). The KiTS19 Challenge had released a dataset of 300 unique kidney cancer patients, with manual annotations done by Climb 4 Kidney Cancer (C4KC). Here we have proposed our new combined cascade deep learning (DL) approach for solving the tasks of the challenge. We used deep learning based detection for localising kidney with the tumor, followed by deep learning based segmentation to create the labels for kidney and tumor locally. Our approach resulted in high recall (96.13) and high Jacquard score (95.4) on the randomly selected 30 volumes that were picked as the validation set.