Automatic multi‐organ segmentation in dual‐energy CT (DECT) with dedicated 3D fully convolutional DECT networks

Abstract

Purpose Dual‐energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single‐energy CT (SECT). Recent research shows that automatic multi‐organ segmentation of DECT data can improve DECT clinical applications. However, most segmentation methods are designed for SECT, while DECT has been significantly less pronounced in research. Therefore, a novel approach is required that is able to take full advantage of the extra information provided by DECT. Methods In the scope of this work, we proposed four three‐dimensional (3D) fully convolutional neural network algorithms for the automatic segmentation of DECT data. We incorporated the extra energy information differently and embedded the fusion of information in each of the network architectures. Results Quantitative evaluation using 45 thorax/abdomen DECT datasets acquired with a clinical dual‐source CT system was investigated. The segmentation of six thoracic and abdominal organs (left and right lungs, liver, spleen, and left and right kidneys) were evaluated using a fivefold cross‐validation strategy. In all of the tests, we achieved the best average Dice coefficients of 98% for the right lung, 98% for the left lung, 96% for the liver, 92% for the spleen, 95% for the right kidney, 93% for the left kidney, respectively. The network architectures exploit dual‐energy spectra and outperform deep learning for SECT. Conclusions The results of the cross‐validation show that our methods are feasible and promising. Successful tests on special clinical cases reveal that our methods have high adaptability in the practical application.

A gentle introduction to deep learning in medical image processing

“…Again, approaches based on convolutional neural networks seem to dominate. Here, we only report Holger Roth's Deeporgan [73], the brain MR segmentation using CNN by Moeskops et al [74], a fully convolutional multi-energy 3-D U-net presented by Chen et al [75], and a U-net-based stent segmentation in X-ray projection domain by Breininger et al [72] as representative examples. Obviously segmentation using deep convolutional networks also works in 2-D as shown by Nirschl et al for histopathologic images [76].…”

Semi-Supervised 3D Abdominal Multi-Organ Segmentation Via Deep Multi-Planar Co-Training

“…Recently, learning-based approaches with relatively large dataset have been introduced for multi-organ segmentation [17,34,8]. Especially, deep Convolutional Neural Networks (CNNs) based methods have achieved a great success in the medical image segmentation [33,10,16,41,42,46,21] in the last few years. Compared with multi-atlas-based approaches, CNNs based methods are generally more efficient and accurate.…”

“…Compared with multi-atlas-based approaches, CNNs based methods are generally more efficient and accurate. CNNs based methods for multi-organ segmentation can be divided into two major categories: 3D CNNs [33,10,16] based and 2D CNNs [41,42,46,21] based. 3D CNNs usually adopt the sliding-window strategy to avoid the out of memory problem, leading to high time complexity.…”

Physics‐driven learning of x‐ray skin dose distribution in interventional procedures

“…For clinical and patient-specific applications, existing CT scansif availablecould be segmented and labeled automatically using a deep learning approach. [45][46][47] As an alternative, Hounsfield units could be mapped to precalibrated densities and tissue labels. Besides, a comparable patient model from a database, such as the XCAT family, could be considered.…”