Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly

IEEE Trans. Biomed. Eng.

Liu

et al. 2018

Self Cite

“…This paper extends a previous conference paper [33] in the following ways. First, a more complete description of the different MAS methods is provided.…”

Section: Introductionsupporting

confidence: 72%

Robust Multicontrast MRI Spleen Segmentation for Splenomegaly Using Multi-Atlas Segmentation

IEEE Trans. Biomed. Eng.

Liu

et al. 2018

Self Cite

“…To alleviate the required manual efforts, previous techniques have been made to perform automatic abdominal organ segmentations for computed tomography (CT) [2–6] and magnetic resonance imaging (MRI) [7–10]. However, segmenting these organs presents unique challenges in that the shape, size, location, and orientation of abdominal organs vary greatly from person to person and even from time to time in the same person (as illustrated in Figure 1) [9]. Additionally, the appearance of those organs depends greatly on the quality of the image, which differs for each scanner.…”

Section: Introductionmentioning

confidence: 99%

Fully convolutional neural networks improve abdominal organ segmentation

Bobo

Bao

Medical Imaging 2018: Image Processing

et al. 2018

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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.

“…However, very limited previous work has been conducted on MRI splenomegaly segmentation, which not only faces the intensity variations but also deals with large spatial and anatomical variations. Recently, Huo et al introduced multi-atlas segmentation (MAS) methods in splenomegaly segmentation on MRI [26,27]. In that method, the L-SIMPLE atlas selection method was designed to overcome the large anatomical and spatial variations of splenomegaly.…”

Section: Introductionmentioning

confidence: 99%

Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks

IEEE Trans. Med. Imaging

Bao

et al. 2019

Self Cite

The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen disease. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to (1) large anatomical and spatial variations of splenomegaly, (2) large inter-and intra-scan intensity variations on multi-modal MRI, and (3) limited numbers of labeled splenomegaly scans. In this paper, we propose the Splenomegaly Segmentation Network (SS-Net) to introduce the deep convolutional neural network (DCNN) approaches in multi-modal MRI splenomegaly segmentation. Large convolutional kernel layers were used to address the spatial and anatomical variations, while the conditional generative adversarial networks (GAN) were employed to leverage the segmentation performance of SS-Net in an end-to-end manner. A clinically acquired cohort containing both T1-weighted (T1w) and T2weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multiatlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network. From the experimental results, the DCNN methods achieved superior performance to the state-of-the-art MAS method. The proposed SS-Net method achieved the highest median and mean Dice scores among investigated baseline DCNN methods.