Despite the progress made in Mamba-based medical image segmentation models, existing methods utilizing unidirectional or multi-directional feature scanning mechanisms struggle to effectively capture dependencies between neighboring positions, limiting the discriminant representation learning of local features. These local features are crucial for medical image segmentation as they provide critical structural information about lesions and organs. To address this limitation, we propose SliceMamba, a simple yet effective locally sensitive Mamba-based medical image segmentation model. SliceMamba features an efficient Bidirectional Slicing and Scanning (BSS) module, which performs bidirectional feature slicing and employs varied scanning mechanisms for sliced features with distinct shapes. This design keeps spatially adjacent features close in the scan sequence, preserving the local structure of the image and enhancing segmentation performance. Additionally, to fit the varying sizes and shapes of lesions and organs, we introduce an Adaptive Slicing Search method that automatically identifies the optimal feature slicing method based on the characteristics of the target data. Extensive experiments on two skin lesion datasets (ISIC2017 and ISIC2018), two polyp segmentation datasets (Kvasir and ClinicDB), one ultra-wide field retinal hemorrhage segmentation dataset (UWF-RHS), and one multi-organ segmentation dataset (Synapse) demonstrate the effectiveness of our method.

One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible.

Background: To investigate the influence of artificial intelligence (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.Methods: We enrolled 196 patents who had undergone both coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1–Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥ 50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with AI system assistance, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of the six readers were calculated at the patient and vessel levels. Additionally, we evaluated the interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader 4, and Reader 5 and Reader 6.Results: The AI system had 94% and 78% sensitivity at the patient and vessel levels, respectively, which were higher than that of Reader 5 and Reader 6. AI-assisted Reader 3 and Reader 4 had higher sensitivity (range + 7.2–+ 16.6% and + 5.9–+ 16.1%, respectively) and NPVs (range + 3.7–+ 13.4% and + 2.7–+ 4.2%, respectively) than Reader 1 and Reader 2 without AI. Good interobserver consistency was found between Reader 3 and Reader 4 in interpreting ≥ 50% stenosis (Kappa value = 0.75 and 0.80 at the patient and vessel levels, respectively). Only Reader 1 and Reader 2 showed poor interobserver consistency (Kappa value = 0.25 and 0.37). Reader 5 and Reader 6 showed moderate agreement (Kappa value = 0.55 and 0.61).Conclusions: Our study showed that using AI could effectively increase the sensitivity of inexperienced readers and significantly improve the consistency of coronary stenosis diagnosis via CCTA.Trial registrationClinical trial registration number: ChiCTR1900021867. Name of registry: Diagnostic performance of artificial intelligence-assisted coronary computed tomography angiography for the assessment of coronary atherosclerotic stenosis.

Background: Deep learning based on segmentation models have been gradually applied in biomedical images and achieved state-of-the-art performance for 3D biomedical segmentation. However, most of existing biomedical segmentation researches take account of the application cases with adapting a single type of medical images from the corresponding examining method. Considering of practical clinic application of the radiology examination for diseases, the multiple image examination methods are normally required for final diagnosis especially in some severe diseases like cancers. Therefore, by considering the cases of employing multi-modal images and exploring the effective multi-modality fusion based on deep networks, we do the research to make full use of complementary information of multi-modal images referring to the clinic experiences of radiologists in image analysis. Methods: Referring to the human radiologist diagnosis experience, we discuss and propose a new self-attention aware mechanism to improve the segmentation performance by paying the different attention on different modal images and different symptoms. Firstly, we propose a multi-path encoder and decoder deep network for 3D biomedical segmentation. Secondly, to leverage the complementary information among different modalities, we introduce a structure of attention mechanism called the Multi-Modality Self-Attention Aware (MMSA) convolution. Multi-modal images we used in the paper are different modalities of MR scanning images, which are input into the network separately. Then self-attention weight fusion of multi-modal features is performed with our proposed MMSA, which can adaptively adjust the fusion weights according to the learned contribution degree of different modalities and different features revealing the different symptoms from the labeled data. Results: Experiments have been done on the public competition dataset BRATS-2015. The results show that our proposed method achieves dice scores of 0.8726, 0.6563, 0.8313 for the whole tumor, the tumor core and the enhancing tumor core, respectively. Comparing with the U-Net with SE block, the scores are increased by 0.0212,0.031,0.0304. Conclusions: We present a multi-modality self-attention aware convolution, which have better segmentation results based on the adaptive weighting fusion mechanism with exploiting the multiple medical image modalities. Experimental results demonstrate the effectiveness of our method and prominent application in the multi-modality fusion based medical image analysis.

Hepatocellular carcinoma (HCC) is a type of primary liver malignant tumor with a high recurrence rate and poor prognosis even undergoing resection or transplantation. Accurate discrimination of the histologic grades of HCC plays a critical role in the management and therapy of HCC patients. In this paper, we discuss a deep learning-based diagnostic model for HCC histologic grading with multimodal Magnetic Resonance Imaging (MRI) images to overcome the problem of limited well-annotated data and extract the discriminated fusion feature referring to the clinical diagnosis experience of radiologists. Accordingly, we propose a novel Multimodality-Contribution-Aware TripNet (MCAT) based on the metric learning and the attention-aware weighted multimodal fusion. The novelty of the method lies in the multimodality small-shot learning architecture designation and the multimodality adaptive weighted computing scheme. The comprehensive experiments are done on the clinic dataset with the well-annotation of lesion location by the professional radiologist. The experimental results show that our proposed MCAT is not only able to achieve acceptable quantitative measuring of HCC histologic grading based on the MRI sequences with small cases but also outperforms previous models in HCC histologic grading, reaching an accuracy of 84 percent, a sensitivity of 87 percent and precision of 89 percent.