Fully Convolutional Network for Liver Segmentation and Lesions Detection

Ben-Cohen, Avi; Diamant, Idit; Klang, Eyal; Amitai, Michal Marianne; Greenspan, Hayit

doi:10.1007/978-3-319-46976-8_9

Cited by 174 publications

(110 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several previous works have shown the superiority of fully convolutional networks for liver lesion detection and segmentation [9,10,11]. Hence, we chose our network model to be U-Net based [12].…”

Section: Network Architecturementioning

confidence: 99%

Anatomical data augmentation for CNN based pixel-wise classification

Ben-Cohen

Klang

Amitai

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

Self Cite

View full text Add to dashboard Cite

In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset contains CT examinations from 140 patients with 333 CT images annotated by an expert radiologist. We tested our approach and compared it to the conventional training process. Results indicate superiority of our method. Using the anatomical data augmentation we achieved an improvement of 3% in the success rate, 5% in the classification accuracy, and 4% in Dice.

show abstract

Section: Network Architecturementioning

confidence: 99%

Anatomical data augmentation for CNN based pixel-wise classification

Ben-Cohen

Klang

Amitai

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many researchers advance this stream using deep learning methods in the liver and tumor segmentation problem and the literature can be classified into two categories broadly. (1) 2D FCNs, such arXiv:1709.07330v3 [cs.CV] 3 Jul 2018 as UNet architecture [15], the multi-channel FCN [16], and the FCN based on VGG-16 [17]. (2) 3D FCNs, where 2D convolutions are replaced by 3D convolutions with volumetric data input [18,19].…”

Section: Introductionmentioning

confidence: 99%

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Chen

et al. 2018

IEEE Trans. Med. Imaging

1,849

974

View full text Add to dashboard Cite

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 in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

show abstract

“…CNNs are fully data-driven and can retrieve hierarchical features automatically by building high-level features from low-level ones, thus obviating the need to manually customize hand-crafted features. Previous works have shown the benefit of using a fully convolutional architecture for liver lesion detection and segmentation applications [2,8]. FCNs can take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.…”

Section: Fully Convolutional Networkmentioning

confidence: 99%

Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection

Ben-Cohen

Klang

Raskin

et al. 2019

Engineering Applications of Artificial Intelligence

Self Cite

169

View full text Add to dashboard Cite

In this work we present a novel system for generation of virtual PET images using CT scans. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to generate simulated PET data from given input CT data. The synthesized PET can be used for false-positive reduction in lesion detection solutions. Clinically, such solutions may enable lesion detection and drug treatment evaluation in a CT-only environment, thus reducing the need for the more expensive and radioactive PET/CT scan. Our dataset includes 60 PET/CT scans from Sheba Medical center. We used 23 scans for training and 37 for testing. Different schemes to achieve the synthesized output were qualitatively compared. Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver. Current results look promising showing a 28% reduction in the average false positive per case from 2.9 to 2.1. The suggested solution is comprehensive and can be expanded to additional body organs, and different modalities. (Avi Ben-Cohen) 1 These authors contributed equally. management of cancer patients and on the other hand the difficulty in providing PET imaging as part of standard imaging, raises a potential need for an alternative, less expensive, fast, and easy to use PET-like imaging.

show abstract

Fully Convolutional Network for Liver Segmentation and Lesions Detection

Cited by 174 publications

References 11 publications

Anatomical data augmentation for CNN based pixel-wise classification

Anatomical data augmentation for CNN based pixel-wise classification

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection

Contact Info

Product

Resources

About