Automatic Semantic Segmentation of the Lumbar Spine. Clinical Applicability in a Multi-parametric and Multi-centre MRI study

• URL: http://arxiv.org/abs/2111.08712v1
• Date: Tue, 16 Nov 2021 17:33:05 GMT
• Title: Automatic Semantic Segmentation of the Lumbar Spine. Clinical Applicability in a Multi-parametric and Multi-centre MRI study
• Authors: Jhon Jairo Saenz-Gamboa (1), Julio Domenech (2), Antonio Alonso-Manjarrez (3), Jon A. G\'omez (4), Maria de la Iglesia-Vay\'a (1 and 5) ((1) FISABIO-CIPF Joint Research Unit in Biomedical Imaging - Val\`encia Spain, (2) Orthopedic Surgery Department Hospital Arnau de Vilanova - Val\`encia Spain, (3) Radiology Department Hospital Arnau de Vilanova - Val\`encia Spain, (4) Pattern Recognition and Human Language Technology research center - Universitat Polit\`ecnica de Val\`encia, (5) Regional ministry of Universal Health and Public Health in Valencia)
• Abstract summary: This document describes the topologies and analyses the results of the neural network designs that obtained the most accurate segmentations. Several of the proposed designs outperform the standard U-Net used as baseline, especially when used in ensembles where the output of multiple neural networks is combined according to different strategies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the major difficulties in medical image segmentation is the high variability of these images, which is caused by their origin (multi-centre), the acquisition protocols (multi-parametric), as well as the variability of human anatomy, the severity of the illness, the effect of age and gender, among others. The problem addressed in this work is the automatic semantic segmentation of lumbar spine Magnetic Resonance images using convolutional neural networks. The purpose is to assign a classes label to each pixel of an image. Classes were defined by radiologists and correspond to different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, and other tissues. The proposed network topologies are variants of the U-Net architecture. Several complementary blocks were used to define the variants: Three types of convolutional blocks, spatial attention models, deep supervision and multilevel feature extractor. This document describes the topologies and analyses the results of the neural network designs that obtained the most accurate segmentations. Several of the proposed designs outperform the standard U-Net used as baseline, especially when used in ensembles where the output of multiple neural networks is combined according to different strategies.

Related papers

• Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
  We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces. We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain. We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
  arXiv  Detail & Related papers  (2024-01-05T19:45:12Z)
• Multilayer Multiset Neuronal Networks -- MMNNs [55.2480439325792]
  The present work describes multilayer multiset neuronal networks incorporating two or more layers of coincidence similarity neurons. The work also explores the utilization of counter-prototype points, which are assigned to the image regions to be avoided.
  arXiv  Detail & Related papers  (2023-08-28T12:55:13Z)
• M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation [73.10707675345253]
  We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image. Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-20T06:26:49Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• Omni-Seg+: A Scale-aware Dynamic Network for Pathological Image Segmentation [13.182646724406291]
  The cross-sectional areas of glomeruli can be 64 times larger than that of peritubular capillaries. We propose the Omni-Seg+ network, a scale-aware dynamic neural network that achieves multi-object (six tissue types) and multi-scale (5X to 40X scale) pathological image segmentation.
  arXiv  Detail & Related papers  (2022-06-27T21:09:55Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis [3.3382651833270587]
  We introduce HybridGNet, an encoder-decoder neural architecture that leverages standard convolutions for image feature encoding and graph convolutional neural networks (GCNNs) to decode plausible representations of anatomical structures. A novel image-to-graph skip connection layer allows localized features to flow from standard convolutional blocks to GCNN blocks, and show that it improves segmentation accuracy.
  arXiv  Detail & Related papers  (2022-03-21T13:37:23Z)
• Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data [6.528287373027917]
  In non-cancer pathology, the learning algorithms can be asked to examine more comprehensive tissue types simultaneously. The prior arts needed to train multiple segmentation networks in order to match the domain-specific knowledge. By learning from 150,000 patch-wise pathological images, the proposed Omni-Seg network achieved superior segmentation accuracy and less resource consumption.
  arXiv  Detail & Related papers  (2021-12-23T16:02:03Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning [13.30545860115548]
  We design a robust and flexible model for cross-stained segmentation. It is able to improve segmentation performance on target type of stained images and use unlabeled data to achieve similar accuracy to labeled data.
  arXiv  Detail & Related papers  (2020-02-20T06:49:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.