Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation

• URL: http://arxiv.org/abs/2408.02043v1
• Date: Sun, 4 Aug 2024 14:30:14 GMT
• Title: Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation
• Authors: Oleksandra Tmenova, Yordanka Velikova, Mahdi Saleh, Nassir Navab,
• Abstract summary: This paper proposes a novel unsupervised deep learning strategy tailored to ultrasound to obtain easily interpretable tissue separations. We integrate key concepts from unsupervised deep spectral methods, which combine spectral graph theory with deep learning methods. We utilize self-supervised transformer features for spectral clustering to generate meaningful segments based on ultrasound-specific metrics and shape and positional priors, ensuring semantic consistency across the dataset.
• Score: 53.37499744840018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ultrasound imaging is challenging to interpret due to non-uniform intensities, low contrast, and inherent artifacts, necessitating extensive training for non-specialists. Advanced representation with clear tissue structure separation could greatly assist clinicians in mapping underlying anatomy and distinguishing between tissue layers. Decomposing an image into semantically meaningful segments is mainly achieved using supervised segmentation algorithms. Unsupervised methods are beneficial, as acquiring large labeled datasets is difficult and costly, but despite their advantages, they still need to be explored in ultrasound. This paper proposes a novel unsupervised deep learning strategy tailored to ultrasound to obtain easily interpretable tissue separations. We integrate key concepts from unsupervised deep spectral methods, which combine spectral graph theory with deep learning methods. We utilize self-supervised transformer features for spectral clustering to generate meaningful segments based on ultrasound-specific metrics and shape and positional priors, ensuring semantic consistency across the dataset. We evaluate our unsupervised deep learning strategy on three ultrasound datasets, showcasing qualitative results across anatomical contexts without label requirements. We also conduct a comparative analysis against other clustering algorithms to demonstrate superior segmentation performance, boundary preservation, and label consistency.

Related papers

• Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation [25.459627476201646]
  We suggest using simple aspect ratio annotations directly from ultrasound clinical diagnoses for automated nodule segmentation. An asymmetric learning framework is developed by extending the aspect ratio annotations with two types of pseudo labels. Experiments on two clinically collected ultrasound datasets (thyroid and breast) demonstrate the superior performance of our proposed method.
  arXiv  Detail & Related papers  (2024-04-23T09:07:04Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization [98.46318529630109]
  We take inspiration from traditional spectral segmentation methods by reframing image decomposition as a graph partitioning problem. We find that these eigenvectors already decompose an image into meaningful segments, and can be readily used to localize objects in a scene. By clustering the features associated with these segments across a dataset, we can obtain well-delineated, nameable regions.
  arXiv  Detail & Related papers  (2022-05-16T17:47:44Z)
• Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks [48.732863591145964]
  We propose a multi-modal convolutional neural network architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure. Our results show a prediction accuracy of 76% at image level on a dataset with 5 different labels.
  arXiv  Detail & Related papers  (2021-10-12T21:22:24Z)
• Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs [11.315178576537768]
  We propose SegGini, a weakly supervised semantic segmentation method using graphs. SegGini segment arbitrary and large images, scaling from tissue microarray (TMA) to whole slide image (WSI)
  arXiv  Detail & Related papers  (2021-03-04T16:04:24Z)
• Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data [123.03252888189546]
  We propose Vicinal Labels Under Uncertainty (VLUU) to bridge the methodological gaps in partially supervised learning (PSL) under data scarcity. Motivated by multi-task learning and vicinal risk minimization, VLUU transforms the partially supervised problem into a fully supervised problem by generating vicinal labels. Our research suggests a new research direction in label-efficient deep learning with partial supervision.
  arXiv  Detail & Related papers  (2020-11-28T16:31:00Z)
• Spectral Analysis Network for Deep Representation Learning and Image Clustering [53.415803942270685]
  This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis. It can identify the local similarities among images in patch level and thus more robust against occlusion. It can learn more clustering-friendly representations and is capable to reveal the deep correlations among data samples.
  arXiv  Detail & Related papers  (2020-09-11T05:07:15Z)

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.