Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

by   Yan Wang, et al.

Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the complexity of the background, and the variable sizes of different organs. To address these challenges, we introduce a novel framework for multi-organ segmentation by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity. OAN is a two-stage deep convolutional network, where deep network features from the first stage are combined with the original image, in a second stage, to reduce the complex background and enhance the discriminative information for the target organs. RCs are added to the first stage to give the lower layers semantic information thereby enabling them to adapt to the sizes of different organs. Our networks are trained on 2D views enabling us to use holistic information and allowing efficient computation. To compensate for the limited cross-sectional information of the original 3D volumetric CT, multi-sectional images are reconstructed from the three different 2D view directions. Then we combine the segmentation results from the different views using statistical fusion, with a novel term relating the structural similarity of the 2D views to the original 3D structure. To train the network and evaluate results, 13 structures were manually annotated by four human raters and confirmed by a senior expert on 236 normal cases. We tested our algorithm and computed Dice-Sorensen similarity coefficients and surface distances for evaluating our estimates of the 13 structures. Our experiments show that the proposed approach outperforms 2D- and 3D-patch based state-of-the-art methods.


page 3

page 6

page 7

page 9

page 12

page 16

page 17

page 18


Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation

Lymphoma detection and segmentation from whole-body Positron Emission To...

Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images

Pulmonary vessel segmentation is important for clinical diagnosis of pul...

A lateral semicircular canal segmentation based geometric calibration for human temporal bone CT Image

Computed Tomography (CT) of the temporal bone has become an important me...

Multi-Atlas Segmentation with Joint Label Fusion of Osteoporotic Vertebral Compression Fractures on CT

The precise and accurate segmentation of the vertebral column is essenti...

Unpaired cross-modality educed distillation (CMEDL) applied to CT lung tumor segmentation

Accurate and robust segmentation of lung cancers from CTs is needed to m...

Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation

Vessel segmentation is widely used to help with vascular disease diagnos...

Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation

Accurate multi-organ abdominal CT segmentation is essential to many clin...

Please sign up or login with your details

Forgot password? Click here to reset