BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision

• URL: http://arxiv.org/abs/2310.07060v1
• Date: Tue, 10 Oct 2023 22:54:01 GMT
• Title: BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision
• Authors: Prantik Deb, Lalith Bharadwaj Baru, Kamalaker Dadi and Bapi Raju S
• Abstract summary: We consider the publicly available dataset ATLAS $v2.0$ to benchmark various end-to-end supervised U-Net style models. Specifically, we have benchmarked models on both 2D and 3D brain images and evaluated them using standard metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain stroke has become a significant burden on global health and thus we need remedies and prevention strategies to overcome this challenge. For this, the immediate identification of stroke and risk stratification is the primary task for clinicians. To aid expert clinicians, automated segmentation models are crucial. In this work, we consider the publicly available dataset ATLAS $v2.0$ to benchmark various end-to-end supervised U-Net style models. Specifically, we have benchmarked models on both 2D and 3D brain images and evaluated them using standard metrics. We have achieved the highest Dice score of 0.583 on the 2D transformer-based model and 0.504 on the 3D residual U-Net respectively. We have conducted the Wilcoxon test for 3D models to correlate the relationship between predicted and actual stroke volume. For reproducibility, the code and model weights are made publicly available: https://github.com/prantik-pdeb/BeSt-LeS.

Related papers

• MedSegMamba: 3D CNN-Mamba Hybrid Architecture for Brain Segmentation [15.514511820130474]
  We develop a 3D patch-based hybrid CNN-Mamba model for subcortical brain segmentation. Our model's performance was validated against several benchmarks.
  arXiv  Detail & Related papers  (2024-09-12T02:19:19Z)
• Efficient Slice Anomaly Detection Network for 3D Brain MRI Volume [2.3633885460047765]
  Current anomaly detection methods excel with benchmark industrial data but struggle with medical data due to varying definitions of 'normal' and 'abnormal' We propose a framework called Simple Slice-based Network (SimpleSliceNet), which utilizes a model pre-trained on ImageNet and fine-tuned on a separate MRI dataset as a 2D slice feature extractor to reduce computational cost.
  arXiv  Detail & Related papers  (2024-08-28T17:20:56Z)
• PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound [6.249772260759159]
  Placenta volume measured from 3D ultrasound (3DUS) images is an important tool for tracking the growth trajectory and is associated with pregnancy outcomes. Manual segmentation is the gold standard, but it is time-consuming and subjective. We propose a lightweight interactive segmentation model aiming for clinical use to interactively segment the placenta from 3DUS images in real-time.
  arXiv  Detail & Related papers  (2024-08-09T22:49:19Z)
• FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models [62.663113296987085]
  Few-shot class-incremental learning aims to mitigate the catastrophic forgetting issue when a model is incrementally trained on limited data. We introduce two novel components: the Redundant Feature Eliminator (RFE) and the Spatial Noise Compensator (SNC) Considering the imbalance in existing 3D datasets, we also propose new evaluation metrics that offer a more nuanced assessment of a 3D FSCIL model.
  arXiv  Detail & Related papers  (2023-12-28T14:52:07Z)
• Image-level supervision and self-training for transformer-based cross-modality tumor segmentation [2.29206349318258]
  We propose a new semi-supervised training strategy called MoDATTS. MoDATTS is designed for accurate cross-modality 3D tumor segmentation on unpaired bi-modal datasets. We report that 99% and 100% of this maximum performance can be attained if 20% and 50% of the target data is annotated.
  arXiv  Detail & Related papers  (2023-09-17T11:50:12Z)
• Uncertainty-Aware Semi-Supervised Learning for Prostate MRI Zonal Segmentation [0.9176056742068814]
  We propose a novel semi-supervised learning (SSL) approach that requires only a relatively small number of annotations. Our method uses a pseudo-labeling technique that employs recent deep learning uncertainty estimation models. Our proposed model outperformed the semi-supervised model in experiments with the ProstateX dataset and an external test set.
  arXiv  Detail & Related papers  (2023-05-10T08:50:04Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Part-Based Models Improve Adversarial Robustness [57.699029966800644]
  We show that combining human prior knowledge with end-to-end learning can improve the robustness of deep neural networks. Our model combines a part segmentation model with a tiny classifier and is trained end-to-end to simultaneously segment objects into parts. Our experiments indicate that these models also reduce texture bias and yield better robustness against common corruptions and spurious correlations.
  arXiv  Detail & Related papers  (2022-09-15T15:41:47Z)
• Recovering 3D Human Mesh from Monocular Images: A Survey [49.00136388529404]
  Estimating human pose and shape from monocular images is a long-standing problem in computer vision. This survey focuses on the task of monocular 3D human mesh recovery.
  arXiv  Detail & Related papers  (2022-03-03T18:56:08Z)
• Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans [72.04652116817238]
  We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification. We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
  arXiv  Detail & Related papers  (2021-01-14T03:45:01Z)
• Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE [66.63629641650572]
  We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices. We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy.
  arXiv  Detail & Related papers  (2020-07-09T13:23:15Z)

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.