Dataset Information

Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI.

ABSTRACT: To help doctors and patients evaluate lumbar intervertebral disc degeneration (IVDD) accurately and efficiently, we propose a segmentation network and a quantitation method for IVDD from T2MRI. A semantic segmentation network (BianqueNet) composed of three innovative modules achieves high-precision segmentation of IVDD-related regions. A quantitative method is used to calculate the signal intensity and geometric features of IVDD. Manual measurements have excellent agreement with automatic calculations, but the latter have better repeatability and efficiency. We investigate the relationship between IVDD parameters and demographic information (age, gender, position and IVDD grade) in a large population. Considering these parameters present strong correlation with IVDD grade, we establish a quantitative criterion for IVDD. This fully automated quantitation system for IVDD may provide more precise information for clinical practice, clinical trials, and mechanism investigation. It also would increase the number of patients that can be monitored.

SUBMITTER: Zheng HD

PROVIDER: S-EPMC8837609 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI.

Zheng Hua-Dong HD Sun Yue-Li YL Kong De-Wei DW Yin Meng-Chen MC Chen Jiang J Lin Yong-Peng YP Ma Xue-Feng XF Wang Hong-Shen HS Yuan Guang-Jie GJ Yao Min M Cui Xue-Jun XJ Tian Ying-Zhong YZ Wang Yong-Jun YJ

Nature communications 20220211 1

To help doctors and patients evaluate lumbar intervertebral disc degeneration (IVDD) accurately and efficiently, we propose a segmentation network and a quantitation method for IVDD from T2MRI. A semantic segmentation network (BianqueNet) composed of three innovative modules achieves high-precision segmentation of IVDD-related regions. A quantitative method is used to calculate the signal intensity and geometric features of IVDD. Manual measurements have excellent agreement with automatic calcul ...[more]

PMID: 35149684

Similar Datasets

Project description:Accumulating evidence has indicated that noncoding RNAs are involved in intervertebral disc degeneration (IDD); however, the competing endogenous RNA (ceRNA)‑mediated regulatory mechanisms in IDD remain rarely reported. The present study aimed to comprehensively investigate the alterations in expression levels of circular RNA (circRNA), long noncoding RNA (lncRNA), microRNA (miRNA/miR) and mRNA in the nucleus pulposus (NP) of patients with IDD. In addition, crucial lncRNA/circRNA‑miRNA‑mRNA ceRNA interaction axes were screened using the GSE67567 microarray dataset obtained from the Gene Expression Omnibus database. After data preprocessing, differentially expressed circRNAs (DECs), lncRNAs (DELs), miRNAs (DEMs) or genes (DEGs) between IDD and normal controls were identified using the Linear Models for Microarray data method. A protein‑protein interaction (PPI) network was constructed for DEGs based on protein databases, followed by module analysis. The ceRNA network was constructed based on the interaction between miRNAs and mRNAs, and lncRNAs/circRNAs and miRNAs. The underlying functions of mRNAs were predicted using the Database for Annotation, Visualization and Integrated Discovery database. The present study identified 636 DECs, 115 DELs, 84 DEMs and 1,040 DEGs between patients with IDD and control individuals. PPI network analysis demonstrated that Fos proto‑oncogene, AP‑1 transcription factor subunit (FOS), mitogen‑activated protein kinase 1 (MAPK1), hypoxia inducible factor 1 subunit α (HIF1A) and transforming growth factor β1 (TGFB1) were hub genes and enriched in modules. Metastasis‑associated lung adenocarcinoma transcript 1 (MALAT1)/hsa_circRNA_102348‑hsa‑miR‑185‑5p‑TGFB1/FOS, MALAT1‑hsa‑miR‑155‑5p‑HIF1A, hsa_circRNA_102399‑hsa‑miR‑302a‑3p‑HIF1A, MALAT1‑hsa‑miR‑519d‑3p‑MAPK1 and hsa_circRNA_100086‑hsa‑miR‑509‑3p‑MAPK1 ceRNA axes were obtained by constructing the ceRNA networks. In conclusion, these identified ceRNA interaction axes may be crucial targets for the treatment of IDD.

Project description:Abnormalities and defects that can cause lumbar spinal stenosis often occur in the Intervertebral Disc (IVD) of the patient's lumbar spine. Their automatic detection and classification require an application of an image analysis algorithm on suitable images, such as mid-sagittal images or traverse mid-height intervertebral disc slices, as inputs. Hence the process of selecting and separating these images from other medical images in the patient's set of scans is necessary. However, the technological progress in making this process automated is still lagging behind other areas in medical image classification research. In this paper, we report the result of our investigation on the suitability and performance of different approaches of machine learning to automatically select the best traverse plane that cuts closest to the half-height of an IVD from a database of lumbar spine MRI images. This study considers images features extracted using eleven different pre-trained Deep Convolution Neural Network (DCNN) models. We investigate the effectiveness of three dimensionality-reduction techniques and three feature-selection techniques on the classification performance. We also investigate the performance of five different Machine Learning (ML) algorithms and three Fully Connected (FC) neural network learning optimizers which are used to train an image classifier with hyperparameter optimization using a wide range of hyperparameter options and values. The different combinations of methods are tested on a publicly available lumbar spine MRI dataset consisting of MRI studies of 515 patients with symptomatic back pain. Our experiment shows that applying the Support Vector Machine algorithm with a short Gaussian kernel on full-length image features extracted using a pre-trained DenseNet201 model is the best approach to use. This approach gives the minimum per-class classification performance of around 0.88 when measured using the precision and recall metrics. The median performance measured using the precision metric ranges from 0.95 to 0.99 whereas that using the recall metric ranges from 0.93 to 1.0. When only considering the L3/L4, L4/L5, and L5/S1 classes, the minimum F1-Scores range between 0.93 to 0.95, whereas the median F1-Scores range between 0.97 to 0.99.

Project description:BackgroundDisc height (DH) change is considered one of the most critical factors in assessing intervertebral disc degeneration (IVD). Pfirrmann et al. developed a scoring system for disc degeneration evaluation based on changes in DH in magnetic resonance imaging (MRI). While the relationship between DH measurements and Pfirrmann scores for disc degeneration has been explored, the validity of different DH measuring techniques or their connection with disc degeneration is yet uncertain. The present study investigates intra-rater and inter-rater agreement and reliability of different DH measurement methods on MRI and evaluates the relationship between different DH measurement methods and Pfirrmann scores of IVD degeneration, as well as between different Pfirrmann scores and clinical outcomes.MethodsAdult patients with MRI scans of the lumbar spine were recruited. Eight DH measuring techniques were tested for intra-rater and inter-rater agreement and reliability. Bland and Altman's Limits of Agreement (LOA) was used to evaluate intra-rater and inter-rater agreements. Intra-rater and inter-rater reliability were evaluated using intra-class correlations (ICC) with 95 % confidence intervals (95 % CI). The association between DH and Pfirrmann scores was examined using one-way ANOVA.ResultsExcellent intra-rater reliability was reported for 332 participants on DH (ranging from 0.912 (0.901, 0.923) to 0.973 (0.964, 0.981) and from 0.902 (0.892, 0.915) to 0.975 (0.962, 0.985) by two independent raters). All measuring methods had high intra-rater agreement, except for methods 4 and 5. All methods had good-to-excellent of inter-rater reliability on DH (ICCs ranging from 0.812 (0.795, 0.828) to 0.995 (0.994, 0.995)) except for the posterior disc material length of method 5 (ICC 0.740 (0.718, 0.761)). Methods 1 to 6 for evaluating DH in patients with spondylolisthesis had poor inter-rater reliability. The IVD levels with grades IV and V in Pfirrmann scores had significantly lower DH than the IVD levels with grades I to III in Pfirrmann scores. IVD levels with grades IV and V in Pfirrmann scores had significantly higher VAS and ODI than IVD levels with grades I in Pfirrmann scores.ConclusionA good-to-excellent intra-rater and inter-rater reliability was achieved on most DH measuring methods on MRI following a standardized and structured protocol. However, small anatomical structures and different tissue borders could influence measurements. Additionally, DH can differentiate between grade IV and V Pfirrmann scores, and severe IVD degeneration (IV and V Pfirrmann) is linked to clinical outcomes.

Dataset Information

Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI.

Publications

Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets