Fully convolutional neural networks improve abdominal organ segmentation

Abstract

Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI’s with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI’s acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI’s with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities.

Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks

“…There are not many studies on the multiorgan segmentation in abdominal MR with DL. Our work demonstrated a better or similar DSC in the abdominal organs to the limited number of existing studies on 0.35-Tesla TrueFISP images 23 and 3.0-Tesla T2-weighted images, 24 especially in challenging organs such as the stomach and the duodenum. Despite a much lower resolution (1.2 vs 0.5 mm), our results are still very competitive compared to multiorgan segmentation on CT images that have better signal to noise ratio and spatial resolution.…”

“…However, there are few studies focused on abdominal MR segmentation. [21][22][23][24] Despite substantial improvement over the years, the performance in automated abdominal MR segmentation still does not match up to the human performance, particularly in complex-structure organs such as the stomach and duodenum. 23 Most of the previous studies utilized two-dimensional (2D) neural networks for organ segmentation.…”

Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning

“…In recent years, deep learning (DL) approaches-convolutional neural networks in particular-have achieved high performance in many areas of computer vision and have been successfully applied in medical imaging (20)(21)(22)(23)(24)(25). A sequence of convolutional layers is applied to the image to optimize segmentation tasks, every convolution can highlight different features, and combining these layers with pooling and nonlinear operations make these networks very powerful.…”

Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia

“…This was similar to a previous study, confirming the superiority of FCNN. 23,24 Meanwhile, similar methods to proof have been used in other studies. Chandak et al used machine learning to improve ensemble docking for drug discovery and compared it with other classical machine learning.…”