Posts Tagged ‘U-Nets’
Complete List of Challenge Submissions
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.
Submission to the Kidney Tumor Segmentation Challenge 2019
Yu, Pengxin
Cui, Xing
Tian, Xi
Ma, Jiechao
Zhang, Rongguo
In this report, we present our method description of the submission to Kidney Tumor Segmentation Challenge 2019. In this challenge, the goal is to segment the kidney and kidney tumor from the CT scans. Our method is based on a common neural architecture U-Net variant, while we pay more attention to the preprocessing stage to better understand the kidney data and postprocessing stage to reduce false positives. The experiments and results show that our proposed methods increase the segmentation accuracy compared to the basic model.
Progressively Training an Enhanced U-Net Model for Segmentation of Kidney Tumors
HE, XueJian
Shun Leung, Ping
Wang, Lu
An enhanced U-Net model with multi-scale inputs and deep supervision are adopted for Kidney tumor segmentation. Focal Tversky Loss is used to train the model, in order to improve the model performance of detecting small tumors. Progressive training is proposed for facilitating model converge. A simple postprocessing method is used to remove segmentation noises. The preliminary results indicate that the proposed model can segment the normal kidney with a satisfactory result; for the tumors with small sizes in low contrast or extreme sizes, there is still a room for improvement.
Kits19 segmentation challenge
Sirazitdinov, Ilyas
Ibragimov, Bulat
In this work we briefly describe our methodology for the kidney segmentation problem.
Kits2019 Challeges: Brief Descriptions of the Algorithm and Process
Wu, Songxiong
Bin, Huang
Abstract. Chronic kidney disease affects many people around the world. Computed tomography (CT) is a widely used imaging modality for kidney disease diagnosis and quantification. However, automatic pathological kidney segmentation is still a challenging task due to large variations in contrast phase, scanning range, pathology, and position in the abdomen, etc. In this work, we propose to combine different Window wide window position as a multi-channel input and Unet, for robust kidney or kidney tumors segmentation.
Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation
O'Reilly, Jamie A.
Manas Sangworasil, Jamie A.
Matsuura, Takenobu
Automated medical image segmentation is a priority research area for computational methods. In particular, detection of cancerous tumors represents a current challenge in this area with potential for real-world impact. This paper describes a method developed in response to the 2019 Kidney Tumor Segmentation Challenge (KiTS19). Axial computed tomography (CT) scans from 210 kidney cancer patients were used to develop and evaluate this automatic segmentation method based on a logical ensemble of fully-convolutional network (FCN) architectures, followed by volumetric validation. Data was pre-processed using conventional computer vision techniques, thresholding, histogram equalization, morphological operations, centering, zooming and resizing. Three binary FCN segmentation models were trained to classify kidney and tumor (2), and only tumor (1), respectively. Model output images were stacked and volumetrically validated to produce the final segmentation for each patient scan. The average F1 score from kidney and tumor pixel classifications was calculated as 0.6758 using preprocessed images and annotations; although restoring to the original image format reduced this score. It remains to be seen how this compares to other solutions.