Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images

Abstract

Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver tumors remains intractable due to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The U-Net has gained increasing popularity recently for image analysis tasks and has shown promising results. Conventional U-Net architectures, however, suffer from three major drawbacks. To cope with these problems, we added a residual path with deconvolution and activation operations to the skip connection of the U-Net to avoid duplication of low resolution information of features. In the case of small object inputs, features in the skip connection are not incorporated with features in the residual path. Furthermore, the proposed architecture has additional convolution layers in the skip connection in order to extract high level global features of small object inputs as well as high level features of high resolution edge information of large object inputs. Efficacy of the modified U-Net (mU-Net) was demonstrated using the public dataset of Liver tumor segmentation (LiTS) challenge 2017. The proposed mU-Net outperformed existing state-of-art networks.

arXiv commentAccept for publication at IEEE Transactions on Medical Imaging

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• TensorFlow: A system for large-scale machine learning
• Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
• A two-stage 3D Unet framework for multi-class segmentation on full resolution image

Cited by in corpus (21)

• U-Net and its variants for medical image segmentation: theory and applications
• A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
• Medical Image Segmentation Using Deep Learning: A Survey
• RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
• Modality specific U-Net variants for biomedical image segmentation: A survey
• Using DUCK-Net for Polyp Image Segmentation
• Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor Segmentation
• PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images
• Data-driven dose calculation algorithm based on deep learning
• Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
• A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision
• HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation
• AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
• MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for Segmentation of Polyps in Colonoscopy
• DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
• FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
• RA V-Net: Deep learning network for automated liver segmentation
• Self-supervised Tumor Segmentation through Layer Decomposition
• Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer
• Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding