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

TL;DR

This paper presents a novel multi-organ segmentation framework for abdominal CT scans using organ-attention networks with reverse connections and statistical fusion, improving accuracy over existing methods.

Contribution

Introduction of organ-attention networks with reverse connections and a statistical fusion approach for multi-view 2D segmentation of 3D abdominal CT scans.

Findings

Outperforms state-of-the-art 2D and 3D segmentation methods.

Achieves high Dice similarity coefficients on 13 abdominal structures.

Utilizes multi-view reconstruction and structural similarity for robust segmentation.

Abstract

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.