Abstract Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks. Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation. The model is developed on a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. We conduct a comprehensive evaluation on 86 internal validation tasks and 60 external validation tasks, demonstrating better accuracy and robustness than modality-wise specialist models. By delivering accurate and efficient segmentation across a wide spectrum of tasks, MedSAM holds significant potential to expedite the evolution of diagnostic tools and the personalization of treatment plans. 

pdf/segment-anything-in-medical-images-5bzsmxjbwo.pdf

Segment anything in medical images

Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, the emergence of SAM will yield significant benefits for a wide array of practical image segmentation applications. In this study, we conduct a series of intriguing investigations into the performance of SAM across various applications, particularly in the fields of natural images, agriculture, manufacturing, remote sensing, and healthcare. We analyze and discuss the benefits and limitations of SAM, while also presenting an outlook on its future development in segmentation tasks. By doing so, we aim to give a comprehensive understanding of SAM's practical applications. This work is expected to provide insights that facilitate future research activities toward generic segmentation. Source code is publicly available.

pdf/segment-anything-is-not-always-perfect-an-investigation-of-jv67fg6j.pdf

Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications

Guía de acceso vascular en hemodiálisis

Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.

https://www.mdpi.com/1424-8220/23/3/1225/pdf?version=1675222392

Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques

Real-time gastric intestinal metaplasia diagnosis tailored for bias and noisy-labeled data with multiple endoscopic imaging

Percutaneous Endovascular Treatment of a Brachial Artery Aneurysm

Ngoc Hoa, et al., Time Trend of Mortality from Esophagus Cancer During 2005-2014 in the Nghe An Province, Viet Nam. Biomed MS.ID.006524. To describe the time trend of esophageal cancer (EC) mortality that occurred in Nghe An province during 2005-2014. Methods: Mortality due to EC and all causes were annually collected from the A6 death register system using the “Validated mortality registration forms” from 2005 to 2014. There was a mandatory monthly active mortality registration by 480 state health stations following the A6 death register system. Age-standardized rates (ASR) of mortality and mortality rate ratio and 95% confidence interval (MRR (95%CI)) by time-period were estimated. Results: Among 140,670 reported deaths, 639 were due to EC (Men 527, women 112). The mortality rate (WHO-ASR) per 100,000 was 4.70 in men and 0.77 in women, giving the men to women ratio was 6.1. Mortality from EC was significantly increased during the 10 years, 2013-2014 vs. 2005-2006, MRR (95%CI): 2.79 (1.95, 3.99), p for trend < 0.05. Conclusion: We observed an increased time trend of mortality due to EC; men are responsible for over 80% of this fatal disease, therefore, a program of primary and secondary prevention against EC focused on men is highly needed in the Nghe An province.

Time Trend of Mortality from Esophagus Cancer During 2005-2014 in the Nghe An Province, Viet Nam

The goal of the Unsupervised Domain Adaptation (UDA) is to transfer the knowledge of the model learned from a source domain with available labels to the target data domain without having access to labels. However, the performance of UDA can greatly suffer from the domain shift issue caused by the misalignment of the two data distributions from the two data sources. Endoscopy can be performed under different light modes, including white-light imaging (WLI) and image-enhanced endoscopy (IEE) light modes. However, most of the current polyp datasets are collected in the WLI mode since it is the standard and most popular one in all endoscopy systems. Therefore, AI models trained on such WLI datasets can strongly degrade when applied to other light modes. In order to address this issue, this paper proposes a coarse-to-fine UDA method that first coarsely aligns the two data distributions at the input level using the Fourier transform in chromatic space; then finely aligns them at the feature level using a fine-grained adversarial training. The backbone of our model is based on a powerful transformer architecture. Experimental results show that our proposed method effectively solves the domain shift issue and achieves a substantial performance improvement on cross-mode polyp segmentation for endoscopy.

A Coarse-to-fine Unsupervised Domain Adaptation Method for Cross-Mode Polyp Segmentation

Endoscopy is one of the most effective methods for diagnosing diseases in the upper GI tract. This paper proposes a unified encoder-decoder model for dealing with three tasks simultaneously: anatomical site classification, lesion classification, and lesion segmentation. In addition, the model can learn from a training set comprised of data from multiple sources. We report results on our own large dataset of 8207 images obtained during routine upper GI endoscopic examinations. Experiments show that our model performs admirably in terms of classification accuracy and yields competitive segmentation results compared to the single-task model with the same architecture.

EndoUNet: A Unified Model for Anatomical Site Classification, Lesion Categorization and Segmentation for Upper Gastrointestinal Endoscopy

In recent years, computer-aided automatic polyp segmentation and neoplasm detection have been an emerging topic in medical image analysis, providing valuable support to colonoscopy procedures. Attentions have been paid to improving the accuracy of polyp detection and segmentation. However, not much focus has been given to latency and throughput for performing these tasks on dedicated devices, which can be crucial for practical applications. This paper introduces a novel deep neural network architecture called BlazeNeo, for the task of polyp segmentation and neoplasm detection with an emphasis on compactness and speed while maintaining high accuracy. The model leverages the highly efficient HarDNet backbone alongside lightweight Receptive Field Blocks and a feature aggregation mechanism for computational efficiency. An auxiliary training strategy is proposed to take full advantage of the training data for the segmentation quality. Our experiments on a challenging dataset show that BlazeNeo achieves improvements in latency and model size while maintaining comparable accuracy against state-of-the-art methods. We obtain over 155 fps while outperforming all compared models in terms of accuracy in INT8 precision when deploying on a dedicated edge device with a conventional configuration.

BlazeNeo: Blazing Fast Polyp Segmentation and Neoplasm Detection

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