Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation
Automatic segmentation of liver and its tumors is an essential step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis and assessment of tumor response to treatment. MICCAI 2017 Liver Tumor Segmentation Challenge (LiTS) provides a common platform for comparing different automatic algorithms on contrast-enhanced abdominal CT images in tasks including 1) liver segmentation, 2) liver tumor segmentation, and 3) tumor burden estimation. We participate this challenge by developing a hierarchical framework based on deep fully convolutional-deconvolutional neural networks (CDNN). A simple CDNN model is firstly trained to provide a quick but coarse segmentation of the liver on the entire CT volume, then another CDNN is applied to the liver region for fine liver segmentation. At last, the segmented liver region, which is enhanced by histogram equalization, is employed as an additional input to the third CDNN for tumor segmentation. Jaccard distance is used as loss function when training CDNN models to eliminate the need of sample re-weighting. Our framework is trained using the 130 challenge training cases provided by LiTS. The evaluation on the 70 challenge testing cases resulted in a mean Dice Similarity Coefficient (DSC) of 0.963 for liver segmentation, a mean DSC of 0.657 for tumor segmentation, and a root mean square error (RMSE) of 0.017 for tumor burden estimation, which ranked our method in the first, fifth and third place, respectively
Code (0)
등록된 구현이 없습니다.
Tasks
Automatic Liver And Tumor SegmentationLiver SegmentationPrognosisSegmentationTumor SegmentationSimilar Papers 제목 키워드 기반
Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering
We present a fully automatic method employing convolutional neural networks based on the 2D U-net architecture and random forest classifier to solve the automatic liver lesion segmentation problem of the ISBI 2017 Liver …
Lesion SegmentationLiver SegmentationOrgan SegmentationSegmentation+1Deep Recurrent Level Set for Segmenting Brain Tumors
Variational Level Set (VLS) has been a widely used method in medical segmentation. However, segmentation accuracy in the VLS method dramatically decreases when dealing with intervening factors such as lighting, shadows, …
Brain Tumor SegmentationSegmentationTumor Segmentation2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation
In this paper we propose a fully automatic 2-stage cascaded approach for segmentation of liver and its tumors in CT (Computed Tomography) images using densely connected fully convolutional neural network (DenseNet). We i…
Automatic Liver And Tumor SegmentationSegmentationTumor SegmentationRA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep convolutional neural networks have become popular in medical image s…
Brain Tumor SegmentationDeep AttentionImage SegmentationMedical Image Segmentation+3H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded …
Automatic Liver And Tumor SegmentationGPUImage SegmentationLesion Segmentation+4