High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey

• URL: http://arxiv.org/abs/2011.08018v2
• Date: Sun, 22 Nov 2020 22:16:52 GMT
• Title: High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey
• Authors: Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah
• Abstract summary: Deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation. Recent research works have focused on incorporating spatial information or prior knowledge to enforce anatomically plausible segmentation.
• Score: 7.685715384001417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tasks. Despite early success, segmentation networks may still generate anatomically aberrant segmentations, with holes or inaccuracies near the object boundaries. To mitigate this effect, recent research works have focused on incorporating spatial information or prior knowledge to enforce anatomically plausible segmentation. If the integration of prior knowledge in image segmentation is not a new topic in classical optimization approaches, it is today an increasing trend in CNN based image segmentation, as shown by the growing literature on the topic. In this survey, we focus on high level prior, embedded at the loss function level. We categorize the articles according to the nature of the prior: the object shape, size, topology, and the inter-regions constraints. We highlight strengths and limitations of current approaches, discuss the challenge related to the design and the integration of prior-based losses, and the optimization strategies, and draw future research directions.

Related papers

• Image Segmentation in Foundation Model Era: A Survey [99.19456390358211]
  Current research in image segmentation lacks a detailed analysis of distinct characteristics, challenges, and solutions associated with these advancements. This survey seeks to fill this gap by providing a thorough review of cutting-edge research centered around FM-driven image segmentation. An exhaustive overview of over 300 segmentation approaches is provided to encapsulate the breadth of current research efforts.
  arXiv  Detail & Related papers  (2024-08-23T10:07:59Z)
• A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design [77.34726150561087]
  We conduct a survey on the most relevant and recent advances in Deep Semantic in the context of vision for autonomous vehicles. Our main objective is to provide a comprehensive discussion on the main methods, advantages, limitations, results and challenges faced from each perspective.
  arXiv  Detail & Related papers  (2023-03-08T01:29:55Z)
• Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation [57.80416375466496]
  Incremental or continual learning has been extensively studied for image classification tasks to alleviate catastrophic forgetting. We propose a simple yet effective method in this paper, named unseen Classes via Regional Objectness for Mining (MicroSeg) Our MicroSeg is based on the assumption that background regions with strong objectness possibly belong to those concepts in the historical or future stages. In this way, the distribution characterizes of old concepts in the feature space could be better perceived, relieving the catastrophic forgetting caused by the background shift accordingly.
  arXiv  Detail & Related papers  (2022-11-13T10:06:17Z)
• Medical Image Segmentation on MRI Images with Missing Modalities: A Review [3.9548535445908928]
  The main goal of this research is to offer a performance evaluation of missing modality compensating networks. Various approaches have been developed over time to mitigate this problem's negative implications.
  arXiv  Detail & Related papers  (2022-03-11T19:33:26Z)
• Effect of Prior-based Losses on Segmentation Performance: A Benchmark [7.6857153840014165]
  Deep convolutional neural networks (CNNs) have demonstrated state-of-the-art performance for medical image segmentation. Recent research studies have focused on incorporating prior knowledge such as object shape or boundary, as constraints in the loss function. In this paper, we establish a benchmark of recent prior-based losses for medical image segmentation.
  arXiv  Detail & Related papers  (2022-01-07T12:27:48Z)
• Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, & Future [0.0]
  This paper will briefly overview some of the state-of-the-art (SoTA) neural network-based segmentation algorithms. It will also demonstrate important clinical implications of effective deep learning-based solutions.
  arXiv  Detail & Related papers  (2021-03-27T19:05:11Z)
• A survey on shape-constraint deep learning for medical image segmentation [0.46023882211671957]
  convolutional deep neural networks and its many variants have completely changed the modern landscape of deep learning based medical image segmentation. The over dependence of these methods on pixel level classification and regression has been identified early on as a problem. To ensure the segmentation result is anatomically consistent, approaches based on Markov/ Conditional Random Fields, Statistical Shape Models are becoming increasingly popular.
  arXiv  Detail & Related papers  (2021-01-19T16:52:10Z)
• 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)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• An Elastic Interaction-Based Loss Function for Medical Image Segmentation [10.851295591782538]
  This paper introduces a long-range elastic interaction-based training strategy for medical image segmentation. In this strategy, CNN learns the target region under the guidance of the elastic interaction energy between the boundary of the predicted region and that of the actual object. Experimental results show that our method is able to achieve considerable improvements compared to commonly used pixel-wise loss functions.
  arXiv  Detail & Related papers  (2020-07-06T11:49:14Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

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.