The Devil Is in the Boundary: Boundary-enhanced Polyp Segmentation

IEEE Transactions on Circuits and Systems for Video Technology, Journal Year: 2024, Volume and Issue: 34(7), P. 5414 - 5423

Published: Jan. 1, 2024

Due to the various appearance of polyps and tiny contrast between polyp area its surrounding background, accurate segmentation has become a challenging task. To tackle this issue, we introduce boundary-enhanced framework for segmentation, called Focused on Boundary Segmentation (FoBS) framework, that leverages multi-level collaboration among sample, feature, optimization. It places greater emphasis boundary improve accuracy segmentation. Firstly, boundary-aware mixup method is designed model's awareness boundary. More importantly, propose deformable laplacian-based feature refining explicitly strengthen representation ability features. employs Laplacian refinement function capture discriminative information from perceptual field, thereby improving adapt variations. In addition, self-adjusting coefficient learning enables adaptive control over strength at each location. Furthermore, develop location-sensitive compensation criterion assigns more importance degraded after during Extensive quantitative qualitative experiments four benchmarks demonstrate effectiveness our automatic Our code available https://github.com/TFboys-lzz/ FoBS.

Language: Английский

---

BreastSAM: adapting the segmentation anything model for breast tumor segmentation in ultrasound imaging

Published: April 1, 2024

Breast cancer is one of the most common cancers among women worldwide, with early detection significantly increasing survival rates. Ultrasound imaging a critical diagnostic tool that aids in by providing real-time breast tissue. We conducted thorough investigation Segment Anything Model (SAM) for task interactive segmentation tumors ultrasound images. explored three pre-trained model variants: ViT_h, ViT_l, and ViT_b, which ViT_l demonstrated superior performance terms mean pixel accuracy, Dice score, IoU score. The significance prompt interaction improving model's was also highlighted, substantial improvements metrics when prompts were incorporated. study further evaluated differential segmenting malignant benign tumors, showing exceptional proficiency both categories, albeit slightly better tumors. Furthermore, we analyzed impacts various tumor characteristics--size, contrast, aspect ratio, complexity--on performance. Our findings reveal contrast size positively impact result, while complex boundaries pose challenges. provides valuable insights using SAM as robust effective algorithm

Language: Английский

---

Full-dose PET synthesis from low-dose PET using 2D high efficiency denoising diffusion probabilistic model

Published: Feb. 16, 2024

The purpose of this study is to reduce radiation exposure in PET imaging while preserving high-quality clinical images. We propose the Consistency Model (PET-CM), an efficient diffusion-model-based approach, estimate full-dose images from low-dose PETs. PET-CM delivers synthetic comparable quality state-of-the-art diffusion-based methods but with significantly higher efficiency. process involves adding Gaussian noise PETs through a forward diffusion and then using U-shaped network (PET-Unet) for denoising reverse process, conditioned on corresponding In experiments one-eighth dose images, achieved MAE 1.321±0.134%, PSNR 33.587±0.674 dB, SSIM 0.960±0.008, NCC 0.967±0.011. scenarios reducing 1/4 full dose, further showcased its capability 1.123±0.112%, 35.851±0.871 0.975±0.003, 0.990±0.003.

Language: Английский

---

EviPrompt: A Training-Free Evidential Prompt Generation Method for Adapting Segment Anything Model in Medical Images

IEEE Transactions on Image Processing, Journal Year: 2024, Volume and Issue: 33, P. 6204 - 6215

Published: Jan. 1, 2024

Medical image segmentation is a critical task in clinical applications. Recently, the Segment Anything Model (SAM) has demonstrated potential for natural segmentation. However, requirement expert labour to provide prompts, and domain gap between medical images pose significant obstacles adapting SAM images. To overcome these challenges, this paper introduces novel prompt generation method named EviPrompt. The proposed requires only single reference image-annotation pair, making it training-free solution that significantly reduces need extensive labelling computational resources. First, prompts are automatically generated based on similarity features of target images, evidential learning introduced improve reliability. Then, mitigate impact gap, committee voting inference-guided in-context employed, generating primarily human prior knowledge reducing reliance extracted semantic information. EviPrompt represents an efficient robust approach We evaluate across broad range tasks modalities, confirming its efficacy. source code available at https://github.com/SPIresearch/EviPrompt.

Language: Английский

---

Progressive Self-Prompting Segment Anything Model for Salient Object Detection in Optical Remote Sensing Images

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 342 - 342

Published: Jan. 20, 2025

With the continuous advancement of deep neural networks, salient object detection (SOD) in natural images has made significant progress. However, SOD optical remote sensing (ORSI-SOD) remains a challenging task due to diversity objects and complexity backgrounds. The primary challenge lies generating robust features that can effectively integrate both global semantic information for localization local spatial details boundary reconstruction. Most existing ORSI-SOD methods rely on pre-trained CNN- or Transformer-based backbones extract from ORSIs, followed by multi-level feature aggregation. Given differences between ORSIs used pre-training, generalization capability these backbone networks is often limited, resulting suboptimal performance. Recently, prompt engineering been employed enhance ability Segment Anything Model (SAM), an emerging vision foundation model achieved remarkable success across various tasks. Despite its success, directly applying SAM without prompts manual interaction unsatisfactory. In this paper, we propose novel progressive self-prompting based SAM, termed PSP-SAM, which generates internal external network overcome limitations ORSI-SOD. Specifically, domain-specific prompting modules, consisting block-shared block-specific adapters, are integrated into learn visual within backbone, facilitating adaptation Furthermore, introduce decoder module performs prompt-guided integration stage-wise mask progressively, enabling prompt-based decoders outside predict saliency maps coarse-to-fine manner. entire trained end-to-end with parameter-efficient fine-tuning. Extensive experiments three benchmark datasets demonstrate our proposed achieves state-of-the-art

Language: Английский

---

ProtoSAM-3D: Interactive semantic segmentation in volumetric medical imaging via a Segment Anything Model and mask-level prototypes

Computerized Medical Imaging and Graphics, Journal Year: 2025, Volume and Issue: 121, P. 102501 - 102501

Published: Feb. 1, 2025

Language: Английский

---

RANet: A receptive aggregation network for polyp segmentation

Multimedia Systems, Journal Year: 2025, Volume and Issue: 31(1)

Published: Feb. 1, 2025

Language: Английский

---

SCABNet: A Novel Polyp Segmentation Network With Spatial‐Gradient Attention and Channel Prioritization

International Journal of Imaging Systems and Technology, Journal Year: 2025, Volume and Issue: 35(2)

Published: Feb. 6, 2025

ABSTRACT Current colorectal polyps detection methods often struggle with efficiency and boundary precision, especially when dealing of complex shapes sizes. Traditional techniques may fail to precisely define the boundaries these polyps, leading suboptimal rates. Furthermore, flat small blend into background due their low contrast against mucosal wall, making them even more challenging detect. To address challenges, we introduce SCABNet, a novel deep learning architecture for efficient polyps. SCABNet employs an encoder‐decoder structure three blocks: Feature Enhancement Block (FEB), Channel Prioritization (CPB), Spatial‐Gradient Boundary Attention (SGBAB). The FEB applies dilation spatial attention high‐level features, enhancing discriminative power improving model's ability capture patterns. CPB, alternative traditional channel blocks, assigns prioritization weights diverse feature channels. SGBAB replaces conventional mechanisms solution that focuses on map. It Jacobian‐based approach construct learned convolutions both vertical horizontal components This allows effectively understand changes in map across different locations, which is crucial detecting complex‐shaped These blocks are strategically embedded within network's skip connections, capabilities without imposing excessive computational demands. They exploit enhance features at levels: high, mid, low, thereby ensuring wide range has been trained Kvasir‐SEG CVC‐ClinicDB datasets evaluated multiple datasets, demonstrating superior results. code available on: https://github.com/KhaledELKarazle97/SCABNet .

Language: Английский

---

Automatic Prompt Generation Using Class Activation Maps for Foundational Models: A Polyp Segmentation Case Study

Machine Learning and Knowledge Extraction, Journal Year: 2025, Volume and Issue: 7(1), P. 22 - 22

Published: Feb. 24, 2025

We introduce a weakly supervised segmentation approach that leverages class activation maps and the Segment Anything Model to generate high-quality masks using only classification data. A pre-trained classifier produces that, once thresholded, yield bounding boxes encapsulating regions of interest. These prompt SAM detailed masks, which are then refined by selecting best overlap with automatically generated from foundational model intersection over union metric. In polyp case study, our outperforms existing zero-shot methods, achieving mean 0.63. This method offers an efficient general solution for image tasks where data scarce.

Language: Английский

---

SAM-MedUS: a foundational model for universal ultrasound image segmentation

Journal of Medical Imaging, Journal Year: 2025, Volume and Issue: 12(02)

Published: Feb. 27, 2025

PurposeSegmentation of ultrasound images for medical diagnosis, monitoring, and research is crucial, although existing methods perform well, they are limited by specific organs, tumors, image devices. Applications the Segment Anything Model (SAM), such as SAM-med2d, use a large number datasets that contain only small fraction images.ApproachIn this work, we proposed SAM-MedUS model generic segmentation utilizes latest publicly available dataset to create diverse containing eight site categories training testing. We integrated ConvNext V2 CM blocks in encoder better global context extraction. In addition, boundary loss function used improve fuzzy boundaries low-contrast images.ResultsExperimental results show outperforms recent on multiple datasets. For more easily adult kidney, it achieves 87.93% IoU 93.58% dice, whereas complex ones infant vein, dice reach 62.31% 78.93%, respectively.ConclusionsWe collected collated an different types achieve uniform images. additional auxiliary branches block enhances ability extract information allows exhibit robust performance excellent generalization ability.

Language: Английский

---