%More specifically, their patch-based architectures often capture superficial textures rather than meaningful anatomical details. speckle  and anisotropic contrast variability and acoustic shadowing Similarly, \cite{wu2024efficientincontextmedicalsegmentation} introduces meta-driven prompt adaptation to improve inference efficiency.
\section{Introduction}
Medical image segmentation is one of the most important tasks in medical imaging, as it is applied for clinical assessment, disease diagnosis and treatment.  After its rapid development, deep learning has become the predominant approach to tackle this challenge. For example, classical convolutional architectures like U-Net \cite{Ronneberger.2015}, W-Net \cite{xia2017wnetdeepmodelfully}, nnU-Net,\cite{isensee2018nnunetselfadaptingframeworkunetbased} and transformer-based variants like TransUNet \cite{chen2021transunettransformersmakestrong} and  UNetTransformer \cite{unetr3d} are widely considered standard baselines. \\
Nevertheless, regarding ultrasound imaging, these models often fail to identify critical anatomical details due to ultrasound characteristics such as speckle noise and high contrast variability and acoustic shadowing \cite{Xiao.2025}.  To enhance generalization across patient populations and imaging conditions, recent studies have explored conditioning segmentation models on a few related examples, referred to as a context set. Methods such as Neuralizer \cite{neuralizer}, Universeg \cite{butoi} and MultiverSeg \cite{wong2025multiversegscalableinteractivesegmentation} leverage contextual priors from a small number of samples. Furthermore,  \cite{gao2025segmentuniversalmedicalimage} developed an in-context learning framework that encodes reference examples into task-specific embeddings to guide a pretrained segmentation model. \\
However, previous in-context learning methods continue to struggle in ultrasound applications. These methods consist of target–context fusion modules, where the target and context feature representations are distorted because of ultrasound characteristics. Thus, their segmentation capabilities are limited. At the same time, foundation models such as SAM2 \cite{ravi2024sam2segmentimages}, MedSAM \cite{Ma.2024}, MEDSAM2 \cite{ma2025medsam2segment3dmedical} and UltraSAM \cite{Meyer_2025} have emerged as universal segmentation frameworks that are capable of generating high-quality masks from simple prompts, like points. While these models have excelled in a wide range of tasks, their performance remains suboptimal in ultrasound segmentation. More specifically, their patch-based architectures cannot mitigate the domain shift between ultrasound data and the general datasets used to train SAM-family models. \\
Consequently, it is understood that the primary factor limiting the performance of all current approaches is their inability to account for the inherent image characteristics of the ultrasound modality. To address these challenges, we propose \textbf{ICL-NoiseUNet}, which introduces a Noise Modulation Block (NMB) mechanism that adaptively refines feature maps based on the ultrasound-specific patterns. Moreover, it employs an In-Context Feature Conditioning (ICFC) module to incorporate context examples and guide feature maps towards expected anatomical structures.  Overall, our main contributions can be summarized as follows: 
\begin{itemize}
\item We introduce a novel context-conditioned segmentation framework that explicitly integrates analytic variance and residual noise maps into the learning process. Thus, our method constitutes a form of noise-aware in-context learning, a subclass that combines contextual guidance with analytic noise modeling to adapt predictions to modality-specific challenges.
\item We design a Noise Modulation Block (NMB) that dynamically adapts model architecture to ultrasound statistics. 
\item We conduct an extensive evaluation across multiple ultrasound segmentation tasks by comparing our method against state-of-the-art context-learning and foundation model architectures.
\end{itemize}
