Multi-Encoder U-Net for Automatic Kidney Tumor Segmentation

Kidney tumor segmentation is a difficult yet critical task for medical image analysis. In recent years, deep learning based methods have achieved many excellent performances in the field of medical image segmentation. In this paper, we propose a Multi-Encoder U-Net segmentation method to tackle the challenging problem of kidney tumor segmentation from CT images. Our Multi-Encoder U-Net method uses three different depth networks as encoders for kidney tumor segmentation: VGG16, ResNet34, ResNet50, a feature fusion networkFED-Net is also used simultaneously, finally fusing the four results. We tested our method on the dataset of MICCAI 2019 Kidney Tumor Segmentation Challenge(KiTS)

Hybrid Cascaded Neural Network for Liver Lesion Segmentation

Automatic liver lesion segmentation is a challenging task while having a significant impact on assisting medical professionals in the designing of effective treatment and planning proper care. In this paper we propose a cascaded system that combines both 2D and 3D convolutional neural networks to effectively segment hepatic lesions. Our 2D network operates on a slice by slice basis to segment the liver and larger tumors, while we use a 3D network to detect small lesions that are often missed in a 2D segmentation design. We employ this algorithm on the LiTS challenge obtaining a Dice score per case of 68.1%, which performs the best among all non pre-trained models and the second best among published methods. We also perform two-fold cross-validation to reveal the over- and under-segmentation issues in the LiTS annotations

A Subabdominal MRI Image Segmentation Algorithm Based on Multi-Scale Feature Pyramid Network and Dual Attention Mechanism

This study aimed to solve the semantic gap and misalignment issue between encoding and decoding because of multiple convolutional and pooling operations in U-Net when segmenting subabdominal MRI images during rectal cancer treatment. A MRI Image Segmentation is proposed based on a multi-scale feature pyramid network and dual attention mechanism. Our innovation is the design of two modules: 1) a dilated convolution and multi-scale feature pyramid network are used in the encoding to avoid the semantic gap. 2) a dual attention mechanism is designed to maintain spatial information of U-Net and reduce misalignment. Experiments on a subabdominal MRI image dataset show the proposed method achieves better performance than others methods. In conclusion, a multi-scale feature pyramid network can reduce the semantic gap, and the dual attention mechanism can make an alignment of features between encoding and decoding.Comment: 19 pages,9 figure