Dataset Information

An electrostatic deformable model for medical image segmentation.

ABSTRACT: A new deformable model, the charged fluid model (CFM), that uses the simulation of charged elements was used to segment medical images. Poisson's equation was used to guide the evolution of the CFM in two steps. In the first step, the elements of the charged fluid were distributed along the propagating interface until electrostatic equilibrium was achieved. In the second step, the propagating front of the charged fluid was deformed in response to the image gradient. The CFM provided sub-pixel precision, required only one parameter setting, and required no prior knowledge of the anatomy of the segmented object. The characteristics of the CFM were compared to existing deformable models using CT and MR images. The results indicate that the CFM is a promising approach for the segmentation of anatomic structures in a wide variety of medical images across different modalities.

SUBMITTER: Chang HH

PROVIDER: S-EPMC2374837 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

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An electrostatic deformable model for medical image segmentation.

Chang Herng-Hua HH Valentino Daniel J DJ

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 20071015 1

A new deformable model, the charged fluid model (CFM), that uses the simulation of charged elements was used to segment medical images. Poisson's equation was used to guide the evolution of the CFM in two steps. In the first step, the elements of the charged fluid were distributed along the propagating interface until electrostatic equilibrium was achieved. In the second step, the propagating front of the charged fluid was deformed in response to the image gradient. The CFM provided sub-pixel pr ...[more]

PMID: 17933492

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Project description:Transformers have demonstrated significant promise for computer vision tasks. Particularly noteworthy is SwinUNETR, a model that employs vision transformers, which has made remarkable advancements in improving the process of segmenting medical images. Nevertheless, the efficacy of training process of SwinUNETR has been constrained by an extended training duration, a limitation primarily attributable to the integration of the attention mechanism within the architecture. In this article, to address this limitation, we introduce a novel framework, called the MetaSwin model. Drawing inspiration from the MetaFormer concept that uses other token mix operations, we propose a transformative modification by substituting attention-based components within SwinUNETR with a straightforward yet impactful spatial pooling operation. Additionally, we incorporate of Squeeze-and-Excitation (SE) blocks after each MetaSwin block of the encoder and into the decoder, which aims at segmentation performance. We evaluate our proposed MetaSwin model on two distinct medical datasets, namely BraTS 2023 and MICCAI 2015 BTCV, and conduct a comprehensive comparison with the two baselines, i.e., SwinUNETR and SwinUNETR+SE models. Our results emphasize the effectiveness of MetaSwin, showcasing its competitive edge against the baselines, utilizing a simple pooling operation and efficient SE blocks. MetaSwin's consistent and superior performance on the BTCV dataset, in comparison to SwinUNETR, is particularly significant. For instance, with a model size of 24, MetaSwin outperforms SwinUNETR's 76.58% Dice score using fewer parameters (15,407,384 vs 15,703,304) and a substantially reduced training time (300 vs 467 mins), achieving an improved Dice score of 79.12%. This research highlights the essential contribution of a simplified transformer framework, incorporating basic elements such as pooling and SE blocks, thus emphasizing their potential to guide the progression of medical segmentation models, without relying on complex attention-based mechanisms.

Project description:Background and objectiveTransformers, which have been widely recognized as state-of-the-art tools in natural language processing (NLP), have also come to be recognized for their value in computer vision tasks. With this increasing popularity, they have also been extensively researched in the more complex medical imaging domain. The associated developments have resulted in transformers being on par with sought-after convolution neural networks, particularly for medical image segmentation. Methods combining both types of networks have proven to be especially successful in capturing local and global contexts, thereby significantly boosting their performances in various segmentation problems. Motivated by this success, we have attempted to survey the consequential research focused on innovative transformer networks, specifically those designed to cater to medical image segmentation in an efficient manner.MethodsDatabases like Google Scholar, arxiv, ResearchGate, Microsoft Academic, and Semantic Scholar have been utilized to find recent developments in this field. Specifically, research in the English language from 2021 to 2023 was considered.Key content and findingsIn this survey, we look into the different types of architectures and attention mechanisms that uniquely improve performance and the structures that are in place to handle complex medical data. Through this survey, we summarize the popular and unconventional transformer-based research as seen through different key angles and analyze quantitatively the strategies that have proven more advanced.ConclusionsWe have also attempted to discern existing gaps and challenges within current research, notably highlighting the deficiency of annotated medical data for precise deep learning model training. Furthermore, potential future directions for enhancing transformers' utility in healthcare are outlined, encompassing strategies such as transfer learning and exploiting foundation models for specialized medical image segmentation.

Dataset Information

An electrostatic deformable model for medical image segmentation.

Publications

An electrostatic deformable model for medical image segmentation.

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