A Region-Based Deep Level Set Formulation for Vertebral Bone Segmentation of Osteoporotic Fractures

Abstract

Accurate segmentation of the vertebrae from medical images plays an important role in computer-aided diagnoses (CADs). It provides an initial and early diagnosis of various vertebral abnormalities to doctors and radiologists. Vertebrae segmentation is very important but difficult task in medical imaging due to low-contrast imaging and noise. It becomes more challenging when dealing with fractured (osteoporotic) cases. This work is dedicated to address the challenging problem of vertebra segmentation. In the past, various segmentation techniques of vertebrae have been proposed. Recently, deep learning techniques have been introduced in biomedical image processing for segmentation and characterization of several abnormalities. These techniques are becoming popular for segmentation purposes due to their robustness and accuracy. In this paper, we present a novel combination of traditional region-based level set with deep learning framework in order to predict shape of vertebral bones accurately; thus, it would be able to handle the fractured cases efficiently. We termed this novel Framework as "FU-Net" which is a powerful and practical framework to handle fractured vertebrae segmentation efficiently. The proposed method was successfully evaluated on two different challenging datasets: (1) 20 CT scans, 15 healthy cases, and 5 fractured cases provided at spine segmentation challenge CSI 2014; (2) 25 CT image data (both healthy and fractured cases) provided at spine segmentation challenge CSI 2016 or xVertSeg.v1 challenge. We have achieved promising results on our proposed technique especially on fractured cases. Dice score was found to be 96.4 ± 0.8% without fractured cases and 92.8 ± 1.9% with fractured cases in CSI 2014 dataset (lumber and thoracic). Similarly, dice score was 95.2 ± 1.9% on 15 CT dataset (with given ground truths) and 95.4 ± 2.1% on total 25 CT dataset for CSI 2016 datasets (with 10 annotated CT datasets). The proposed technique outperformed other state-of-the-art techniques and handled the fractured cases for the first time efficiently.

Keywords: Computer-aided diagnosis; Deep learning; Medical image analysis; Vertebrae segmentation; Vertebral osteoporotic fracture.

Fig. 1

Human vertebral column

Fig. 2

Osteoporotic vertebral fractures are shown in a and b with arrows

Fig. 3

Statistics of death cases with osteoporotic fractures in year 2010 on around 30 European countries data

Fig. 4

WHO estimated statistics of osteoporotic fractured cases

Fig. 5

System diagram of FU-Net

Fig. 6

U-Net architecture. a Segmentation network architecture. b Labels

Fig. 7

Examples of datasets. a CSI 2014 Data Set. b CSI 2014 Dataset with fractured bone. c CSI 2016 Dataset with fractures

Fig. 8

Segmentation results of our proposed system (a, b). Results of CSI 2014 CT dataset. c Result from fractured case from CSI 2014 dataset (cropped for better visualization); arrow shows the fractured bone. d Result of xVertSeg.v1 dataset. e Result of fractured case of xVertSeg.v1 dataset with arrow, pointing the fractured bones

Fig. 9

Box and whisker plot on evaluation set of CSI 2014 dataset (including fractured cases)

Fig. 10

Mean dice score evaluation on various combinations of training data