Posts Tagged ‘convolutional neural networks’
Complete List of Challenge Submissions
Convolutional Neural Network for Kidney and kidney Tumor segmentation
Kumar Anand, Vikas
Aurangabadkar, Pranav
Khened, Mahendra
Krishnamurthi, Ganapathy
In this work, we have attempted to develop algorithms for automatic segmentation of kidney and kidney tumous from CT images. We have exploited encoder decoder architecture of fully convolutional neural network.Pre-processesing steps involves slice extraction, data standardization and Hounsfield unit windowing. The proposed network has been trained on CT images of kidney and kidney tumors with their ground truth. Weighted combination of focal loss and dice loss has been minimized using Adam as optimizer. Dice coefficient of 94.68% and 94.51% has been achieved for kidney and kidney tumor segmentation respectively.
Convolution Neural Network Application in Kidney Tumor Segmentation on CT Images
Hunag, Jianping
Lin, Zefang
In this paper, we propose an novel network model which is similar to V-net and prove its superiority and efficiency in tumor segmentation. And The model of segmentation of Kidney is Dense V-Network [1]. Then we ensemble the results of two networks together to get a final predict result for kidney and tumor. In particularly, we apply a series of method to image preprocessing, which is proved to be effective in improving dice.
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.
Semantic Segmentation of Kidney Tumor using Convolutional Neural Networks
Daza, Laura
Gómez, Catalina
Arbeláez, Pablo
We present a fully automatic method for segmentation of kidney tumors in CT volumetric data based on DeepLab v3+, the stateof-the-art model in semantic segmentation in natural images. We adapt the architecture to process medical data and reduce the computational complexity to allow training 3D models. We evaluate our approach on the Kidney Tumor Segmentation Challenge 2019 dataset, and define a validation set to experiment with the model’s parameters. In our validation set, we report a dice score of XX for the kidney class and YY for the tumor class.