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3D medical image segmentation is critical for clinical diagnosis and treatment planning. Recently, with the powerful generalization, the foundational segmentation model SAM is widely used in medical images. However, the existing SAM variants still ...

Whole brain segmentation, which divides the entire brain volume into anatomically labeled regions of interest (ROIs), is a crucial step in brain image analysis. Traditional methods often rely on intricate pipelines that, while accurate, are time-...

Medical image segmentation is crucial in the field of medical imaging, assisting healthcare professionals in analyzing images and improving diagnostic performance. Recent advancements in Transformer-based networks, which utilize self-attention ...

Accurate quantification of multiple sclerosis (MS) lesions using multi-contrast magnetic resonance imaging (MRI) plays a crucial role in disease assessment. While many methods for automatic MS lesion segmentation in MRI are available, these ...

Effectively segmenting Crohn’s disease (CD) from computed tomography is crucial for clinical use. Given the difficulty of obtaining manual annotations, more and more researchers have begun to pay attention to weakly supervised methods. However, ...

Deep learning-based segmentation models have made remarkable progress in aiding pulmonary disease diagnosis by segmenting lung lesion areas in large amounts of annotated X-ray images. Recently, to alleviate the demand for medical image data and ...

The design of activation functions constitutes a cornerstone for deep learning (DL) applications, exerting a profound influence on the performance and capabilities of neural networks. This influence stems from their ability to introduce non-...

Accurate vessel segmentation in Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images is crucial for diagnosing retinal diseases. Although recent techniques have shown encouraging outcomes in vessel segmentation, models trained on one ...

Airway segmentation in chest computed tomography (CT) images is critical for tracheal disease diagnosis and surgical navigation. However, airway segmentation is challenging due to complex tree structures and branches of different sizes. To enhance ...

Nuclei semantic segmentation is a key component for advancing machine learning and deep learning applications in digital pathology. However, most existing segmentation models are trained and tested on high-quality data acquired with expensive ...

Deep learning models have exhibited remarkable efficacy in accurately delineating the prostate for diagnosis and treatment of prostate diseases, but challenges persist in achieving robust generalization across different medical centers. Source-...

Polyp segmentation plays a pivotal role in colorectal cancer diagnosis. Recently, the emergence of the Segment Anything Model (SAM) has introduced unprecedented potential for polyp segmentation, leveraging its powerful pre-training capability on ...

The paraspinal muscles are crucial for spinal stability, which can be quantitatively analyzed through image segmentation. However, unclear muscle boundaries, severe deformations, and limited training data impose great challenges for existing ...

Deep neural networks for medical image segmentation often produce overconfident results misaligned with empirical observations. Such miscalibration challenges their clinical translation. We propose to use marginal L1 average calibration error (mL1-...

In the study of skin lesion segmentation, models based on convolution neural networks (CNN) and vision transformers (ViT) have been extensively explored but face challenges in capturing fine details near boundaries. The advent of Diffusion ...

Due to blurred boundaries between the background and the foreground, along with the overlapping of different tumor lesions, accurate segmentation of brain tumors presents significant challenges. To tackle these issues, we propose a causal ...

Interactive segmentation techniques are in high demand in medical imaging, where the user-machine interactions are to address the imperfections of a model and to speed up the manual annotation. All recently proposed interactive approaches have ...

We introduce a conditional implicit neural atlas (CINA) for spatio-temporal atlas generation from Magnetic Resonance Images (MRI) of the neurotypical and pathological fetal brain, that is fully independent of affine or non-rigid registration. ...

Pneumonia, recognized as a severe respiratory disease, has attracted widespread attention in the wake of the COVID-19 pandemic, underscoring the critical need for precise diagnosis and effective treatment. Despite significant advancements in the ...

Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention and latent embedding (cDAL) for medical image segmentation. In cDAL,...

Motion artifacts caused by prolonged acquisition time are a significant challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue segmentation. These artifacts appear as blurred images that mimic tissue-like appearances, making ...

Nuclei segmentation in cervical cell images is a crucial technique for the automatic diagnosis of cervical cell pathology. The current state-of-the-art (SOTA) nuclei segmentation methods often require significant time and resources to provide ...

Performance of deep learning segmentation models is significantly challenged in its transferability across different medical imaging domains, particularly when aiming to adapt these models to a target domain with insufficient annotated data for ...

MLP–based networks, while being lighter than traditional convolution– and transformer–based networks commonly used in medical image segmentation, often struggle with capturing local structures due to the limitations of fully–connected (FC) layers, ...

In-context learning (ICL) with Large Vision Models (LVMs) presents a promising avenue in medical image segmentation by reducing the reliance on extensive labeling. However, the ICL performance of LVMs highly depends on the choices of visual ...

Convolutional neural networks have primarily led 3D medical image segmentation but may be limited by small receptive fields. Transformer models excel in capturing global relationships through self-attention but are challenged by high computational ...

Assessment of the glomerular basement membrane (GBM) in transmission electron microscopy (TEM) is crucial for diagnosing chronic kidney disease (CKD). The lack of domain-independent automatic segmentation tools for the GBM necessitates an AI-based ...