Day Case Lumbar Fusion Surgery
The article discusses the challenges faced in the diagnosis and positioning of lumbar disc diseases and proposes a novel Multi-modal Assistant Segmentation Network (MAS-Net) for accurately and intelligently segmenting and locating Lumbar Intervertebral Discs (LIDs). The MAS-Net architecture includes components such as the Multi-Branch Fusion Encoder, Cross-Modality Correlation Evaluation, Channel Fusion Transformer, and Selective Kernel based decoder. The proposed MAS-Net outperformed existing networks and achieved a high Dice coefficient, accurately segmenting the LIDs and generating a precise 3D model. The use of multi-modal Magnetic Resonance Images (MRIs) improves the accuracy of LID segmentation and simplifies the diagnostic process, addressing challenges faced by doctors
Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : most established spine centre on Harley Street UK
Published article
OBJECTIVE: Despite advancements in medical imaging technology, the diagnosis and positioning of lumbar disc diseases still heavily rely on the expertise and experience of medical professionals. This process is often time-consuming, labor-intensive, and susceptible to subjective factors. Achieving automatic positioning and segmentation of Lumbar Intervertebral Disc(LID) is the first and critical step in intelligent diagnosis of lumbar disc diseases. However, due to the complexity of the vertebral…
Lumbar Fusion Surgery Expert. Best Spinal Surgeon UK
Phys Med Biol. 2023 Aug 11. doi: 10.1088/1361-6560/acef9f. Online ahead of print.ABSTRACTOBJECTIVE: Despite advancements in medical imaging technology, the diagnosis and positioning of lumbar disc diseases still heavily rely on the expertise and experience of medical professionals. This process is often time-consuming, labor-intensive, and susceptible to subjective factors. Achieving automatic positioning and segmentation of Lumbar,
Phys Med Biol. 2023 Aug 11. doi: 10.1088/1361-6560/acef9f. Online ahead of print.
ABSTRACT
OBJECTIVE: Despite advancements in medical imaging technology, the diagnosis and positioning of lumbar disc diseases still heavily rely on the expertise and experience of medical professionals. This process is often time-consuming, labor-intensive, and susceptible to subjective factors. Achieving automatic positioning and segmentation of Lumbar Intervertebral Disc(LID) is the first and critical step in intelligent diagnosis of lumbar disc diseases. However, due to the complexity of the vertebral body and the ambiguity of the soft tissue boundaries of the LID, accurate and intelligent segmentation of LIDs remains challenging. The study aims to accurately and intelligently segment and locate LIDs by fully utilizing multi-modal Lumbar Magnetic Resonance Images(MRIs).
APPROACH: A novel Multi-modal Assistant Segmentation Network(MAS-Net) is proposed in this paper. The architecture consists of four key components: the Multi-Branch Fusion Encoder(MBFE), the Cross-Modality Correlation Evaluation(CMCE), the Channel Fusion Transformer(CFT), and the Selective Kernel(SK) based decoder. The MBFE module captures and integrates various modal features, while the CMCE module facilitates the fusion process between the MBFE and decoder. The CFT module selectively guides the flow of information between the MBFE and decoder and effectively utilizes skip connections from multiple layers. The SK module computes the significance of each channel using global pooling operations and applies weights to the input feature maps to improve the model’s recognition of important features.
MAIN RESULTS: The proposed MAS-Net achieved a Dice coefficient of 93.08% on IVD3Seg and 93.22% on DMD dataset, outperforming the current state-of-the-art network, accurately segmenting the LIDs, and generating a 3D model that can precisely display the LIDs.
SIGNIFICANCE: MAS-Net automates the diagnostics process and addresses challenges faced by doctors. Simplifying and enhancing the clarity of visual representation, multi-modal MRI allows for better information complementation and LIDs segmentation. By successfully integrating data from various modalities, the accuracy of LID segmentation is improved.
PMID:37567228 | DOI:10.1088/1361-6560/acef9f
The London Spine Unit : most established spine centre on Harley Street UK
Read the original publication:
MAS-Net:multi-modal assistant segmentation network for lumbar intervertebral disc
