To provide an efficient and accurate clinical tool to aid in the diagnosis of neural foramina stenosis.
We propose a novel boundary regression segmentation framework for fully automated, multi-modal segmentation and area estimation of neural foramina.
Fig. 1. SSR model, implemented by integrating class label (0:normal,1:stenosed) into manifold alignment, provides a discriminative feature space (called SSR space) for reliable classification. (a) the class overlapping problem in original image space; (b) stenosed SSR; (c) normal SSR; (d) SSR space.[2]
We propose a newly designed DMML-Net integrating multi-output learning and multitask regression learning into a fully convolutional network.[3]
Fig. 2. DMML-Net directly localizes and grades all lumbar organs after importing clinical lumbar MRI scans. Multiscale multi-output learning predicts target organs at many convolutional layers of an FCN. Multitask regression learning links multiple tasks with parameter sharing for mutual benefit[3]
Formulates the segmentation task as a boundary regression model to fully leverage the advancement of machine learning in a holistic fashion for seeking the optimal segmentation, which simultaneously preserves accuracy, robustness, and efficiency.
The propose framework contains the following two components:
Fig. 3. The overview of our regression segmentation framework.[1]
Fig. 4. The superiority over conventional model-based methods. (a) Unsatisfied results achieved by conventional model-based methods; (b) accurate results achieved by our regression segmentation.[1]
The propose framework contains the following three components:
Fig. 2. The overview of our automated diagnosis framework.[2]
Fig. 3. Accurate diagnosis results in multiple subjects with diverse appearance, size, and shape.[2]
Fig. 4. The main workflow of DMML-Net. The shadow region covers the multiscale multi-output learning architecture, while the unshaded region covers the multitask regression learning.[3]
Fig. 5. Feature boosting module merges selected convolutional layers to generate output layers. It enhances the global context information and exchange information between scales, thus it is capable of the scale-invariance for various target organs.[3]
Fig. 6. The main procedures of the multitask regression learning formulation.[3]
Fig. 7. Default box proposal module is responsible for hypothesizing locations of organs. a An input MRI scan with part ground truth boxes which are only needed in training phase. b Default box proposal module embeds k-means clustering results into default boxes proposal at each location of the 64 × 64 feature map. More refined locations (loc) of default boxes will be predicted by multitask regression module.[3]
Fig. 9. The combination of multitask regression module and multitask loss module has combated the huge challenges from the localization and grading of all lumbar organs. These stable precision-recall curves also demonstrate the strengths of DMML-Net in the pathogenesisbased diagnosis of LNFS since both normal and abnormal neural foramina (NF) have high recall and precision.[3]
Boundary regression model was validated on a dataset including 912 MR and 306 CT images collected from 152 subjects. Two types of metrics were used for evaluation: metric in segmentation, the dice similarity coefficiet, and a metric in area estimation, boundary distance.[1]
SSR validation was conducted on a dataset including 110 mid-sagittal MR spine images collected from 110 subjects. Training images were manually cropped and labelled by physicians. Classification accuracy, specificity, and sensitivity were reported.[2]
DMML-Net validation was conducted on a dataset including 1200 neural formina (518 normal, 682 abnormal), 1200 discs (627 normal, 573 abnormal), and 1000 lumbar vertebrae (690 normal, 310 abnormal) from 200 patients (average 60 years). Standard five-fold cross-validation was employed for performancec evaluation and comparison. Data augmentation, such as random distortion, random flip, random brightness, and contrast adjustment was also employed to enhance generalization. To demonstrate the DMML-Net's sensitivity and interpretability a standard precision-recall curve and mean average precision were reported.[3]
1. Xiaoxu He, Andrea Lum, Manas Sharma, Gary Brahm, Ashley Mercado, and Shuo Li. Automated Segmentation and Area Estimation of Neural Foramina with Boundary Regression Model. Pattern Recognition, 63, 625-641. doi:10.1016/j.patcog.2016.09.018. 2016.
2. Xiaoxu He, Yilong Yin, Manas Sharma, Gary Brahm, Ashley Mercado, and Shuo Li. Automated Diagnosis of Neural Foraminal Stenosis Using Synchronized Superpixels Representation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 Lecture Notes in Computer Science, 335-343. doi:10.1007/978-3-319-46723-8_39. 2016.
3. Zhongyi Han, Benzheng Wei, Stephanie Leung, Ilanit Ben Nachum, David Laidley, and Shuo Li. Automated Pathogenesis-based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning. Neuroinformatics, In Press, Online Available. 2018.
4. Xiaoxu He, Stephanie Leung, James Warrington, Olga Shmuilovich, and Shuo Li, Automated Neural Foraminal Stenosis Grading via Task-aware Structural Representation Learning. Neurocomputing, 287: 185-195, 2018.