Efficient two-step liver and tumour segmentation on abdominal CT via deep learning and a conditional random field

Artificial intelligence model for tumoral clinical decision support systems

BACKGROUND AND OBJECTIVE

Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient-specific normal features and pathologies. A key distinction from previous models lies in its ability to produce enriched image descriptors solely from binary information, eliminating the need for costly and difficult to obtain tumor segmentation.

METHODS

The proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision-making processes. This generalization makes possible for future research in different medical diagnosis areas with almost not any change in the system.

RESULTS

We conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain Magnetic Resonances (MRs), achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. Our findings highlight the significant potential for improving the efficiency and accuracy of comparative diagnostics and the treatment of tumoral pathologies.

CONCLUSIONS

This paper provides substantial grounds for further exploration of the broader applicability and optimization of the proposed architecture to enhance clinical decision-making. The novel approach presented in this work marks a significant advancement in the field of medical diagnosis, particularly in the context of Artificial Intelligence-assisted image retrieval, and promises to reduce costs and improve the quality of patient care using Artificial Intelligence as a support tool instead of a black box system.

Towards accurate abdominal tumor segmentation: A 2D model with Position-Aware and Key Slice Feature Sharing

Abdominal tumor segmentation is a crucial yet challenging step during the screening and diagnosis of tumors. While 3D segmentation models provide powerful performance, they demand substantial computational resources. Additionally, in 3D data, tumors often represent a small portion, leading to imbalanced data and potentially overlooking crucial information. Conversely, 2D segmentation models have a lightweight structure, but disregard the inter-slice correlation, risking the loss of tumor in edge slices. To address these challenges, this paper proposes a novel Position-Aware and Key Slice Feature Sharing 2D tumor segmentation model (PAKS-Net). Leveraging the Swin-Transformer, we effectively model the global features within each slice, facilitating essential information extraction. Furthermore, we introduce a Position-Aware module to capture the spatial relationship between tumors and their corresponding organs, mitigating noise and interference from surrounding organ tissues. To enhance the edge slice segmentation accuracy, we employ key slices to assist in the segmentation of other slices to prioritize tumor regions. Through extensive experiments on three abdominal tumor segmentation CT datasets and a lung tumor segmentation CT dataset, PAKS-Net demonstrates superior performance, reaching 0.893, 0.769, 0.598 and 0.738 tumor DSC on the KiTS19, LiTS17, pancreas and LOTUS datasets, surpassing 3D segmentation models, while remaining computationally efficient with fewer parameters.

Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm

BACKGROUND

CT-image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time-consuming process and inter-observer variations of manual segmentation have limited wider application in clinical practice.

PURPOSE

Our study aimed to propose an automated deep learning (DL) segmentation algorithm for liver and hepatic vessels on portal venous phase CT images.

METHODS

This retrospective study was performed to develop a coarse-to-fine DL-based algorithm that was trained, validated, and tested using private 413, 52, and 50 portal venous phase CT images, respectively. Additionally, the performance of the DL algorithm was extensively evaluated and compared with manual segmentation using an independent clinical dataset of preoperative contrast-enhanced CT images from 44 patients with hepatic focal lesions. The accuracy of DL-based segmentation was quantitatively evaluated using the Dice Similarity Coefficient (DSC) and complementary metrics [Normalized Surface Dice (NSD) and Hausdorff distance_95 (HD95) for liver segmentation, Recall and Precision for hepatic vessel segmentation]. The processing time for DL and manual segmentation was also compared.

RESULTS

Our DL algorithm achieved accurate liver segmentation with DSC of 0.98, NSD of 0.92, and HD95 of 1.52 mm. DL-segmentation of hepatic veins, portal veins, and inferior vena cava attained DSC of 0.86, 0.89, and 0.94, respectively. Compared with the manual approach, the DL algorithm significantly outperformed with better segmentation results for both liver and hepatic vessels, with higher accuracy of liver and hepatic vessel segmentation (all p < 0.001) in independent 44 clinical data. In addition, the DL method significantly reduced the manual processing time of clinical postprocessing (p < 0.001).

CONCLUSIONS

The proposed DL algorithm potentially enabled accurate and rapid segmentation for liver and hepatic vessels using portal venous phase contrast CT images.

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.

PA-Net: A phase attention network fusing venous and arterial phase features of CT images for liver tumor segmentation

BACKGROUND AND OBJECTIVE

Liver cancer seriously threatens human health. In clinical diagnosis, contrast-enhanced computed tomography (CECT) images provide important supplementary information for accurate liver tumor segmentation. However, most of the existing methods of liver tumor automatic segmentation focus only on single-phase image features. And the existing multi-modal methods have limited segmentation effect due to the redundancy of fusion features. In addition, the spatial misalignment of multi-phase images causes feature interference.

METHODS

In this paper, we propose a phase attention network (PA-Net) to adequately aggregate multi-phase information of CT images and improve segmentation performance for liver tumors. Specifically, we design a PA module to generate attention weight maps voxel by voxel to efficiently fuse multi-phase CT images features to avoid feature redundancy. In order to solve the problem of feature interference in the multi-phase image segmentation task, we design a new learning strategy and prove its effectiveness experimentally.

RESULTS

We conduct comparative experiments on the in-house clinical dataset and achieve the SOTA segmentation performance on multi-phase methods. In addition, our method has improved the mean dice score by 3.3% compared with the single-phase method based on nnUNet, and our learning strategy has improved the mean dice score by 1.51% compared with the ML strategy.

CONCLUSION

The experimental results show that our method is superior to the existing multi-phase liver tumor segmentation method, and provides a scheme for dealing with missing modalities in multi-modal tasks. In addition, our proposed learning strategy makes more effective use of arterial phase image information and is proven to be the most effective in liver tumor segmentation tasks using thick-layer CT images. The source code is released on (https://github.com/Houjunfeng203934/PA-Net).

Investigation of lethal thresholds of nanosecond pulsed electric field in rabbit VX2 hepatic tumors through finite element analysis and verification with a single-needle bipolar electrode: A prospective strategy employing three-dimensional comparisons

Pulsed electric field has emerged as a promising modality for the solid tumor ablation with the advantage in treatment planning, however, the accurate prediction of the lesion margin requires the determination of the lethal electric field (E) thresholds. Herein we employ the highly repetitive nanosecond pulsed electric field (RnsPEF) to ablate the normal and VX2 tumor-bearing livers of rabbits. The ultrasound-guided surgery is operated using the conventional double- and newly devised single-needle bipolar electrodes. Finite element analysis is also introduced to simulate the E distribution in the practical treatments. Two- and three-dimensional investigations are performed on the image measurements and reconstructed calcification models on micro-CT, respectively. Specially, an algorithm considering the model surface, volume and shape is employed to compare the similarities between the simulative and experimental models. Blood vessel injury, temperature and synergistic efficacy with doxorubicin (DOX) are also investigated. According to the three-dimensional calculation, the overall E threshold is 4536.4 ± 618.2 V/cm and the single-needle bipolar electrode is verified to be effective in tissue ablation. Vessels are well preserved and the increment of temperature is limited. Synergy of RnsPEF and DOX shows increased apoptosis and improved long-term tumor survival. Our study presents a prospective strategy for the evaluation of the lethal E threshold, which can be considered to guide the future clinical treatment planning for RnsPEF.

MS-FANet: Multi-scale feature attention network for liver tumor segmentation

Accurate segmentation of liver tumors is a prerequisite for early diagnosis of liver cancer. Segmentation networks extract features continuously at the same scale, which cannot adapt to the variation of liver tumor volume in computed tomography (CT). Hence, a multi-scale feature attention network (MS-FANet) for liver tumor segmentation is proposed in this paper. The novel residual attention (RA) block and multi-scale atrous downsampling (MAD) are introduced in the encoder of MS-FANet to sufficiently learn variable tumor features and extract tumor features at different scales simultaneously. The dual-path feature (DF) filter and dense upsampling (DU) are introduced in the feature reduction process to reduce effective features for the accurate segmentation of liver tumors. On the public LiTS dataset and 3DIRCADb dataset, MS-FANet achieved 74.2% and 78.0% of average Dice, respectively, outperforming most state-of-the-art networks, this strongly proves the excellent liver tumor segmentation performance and the ability to learn features at different scales.

Fractal nature of human gastrointestinal system: Exploring a new era

The morphological complexity of cells and tissues, whether normal or pathological, is characterized by two primary attributes: Irregularity and self-similarity across different scales. When an object exhibits self-similarity, its shape remains unchanged as the scales of measurement vary because any part of it resembles the whole. On the other hand, the size and geometric characteristics of an irregular object vary as the resolution increases, revealing more intricate details. Despite numerous attempts, a reliable and accurate method for quantifying the morphological features of gastrointestinal organs, tissues, cells, their dynamic changes, and pathological disorders has not yet been established. However, fractal geometry, which studies shapes and patterns that exhibit self-similarity, holds promise in providing a quantitative measure of the irregularly shaped morphologies and their underlying self-similar temporal behaviors. In this context, we explore the fractal nature of the gastrointestinal system and the potential of fractal geometry as a robust descriptor of its complex forms and functions. Additionally, we examine the practical applications of fractal geometry in clinical gastroenterology and hepatology practice.

Measuring distance from lowest boundary of rectal tumor to anal verge on CT images using pyramid attention pooling transformer

Accurately measuring the Distance from the lowest boundary of rectal tumor To the Anal Verge (DTAV) is critical for developing optimal surgical plans for treating patients with rectal cancer. DTAV was traditionally estimated by colonoscopy or manual measurement on computed tomography (CT) images. However, colonoscopy brings substantial pains to the patient. As for manual measurement on CT images, it is time-consuming and its accuracy depends on the surgeon's expertise. In this work, we present a novel method for automatically measuring DTAV from sagittal CT images. The success of our method is mainly credited to a pyramid attention pooling (PAP) transformer architecture, which naturally entangles global lesion localization and local boundary delineation. Our method automatically generates the rectum's centerline based on a segmented rectum and tumor image to simulate the manual measurement of DTAV. We conduct a comprehensive evaluation of the method with a newly collected rectum tumor CT image dataset. On a test dataset of 48 patients' CT images with rectal tumors, the mean absolute difference between our method and the gold standard is 1.74 cm, which is a significant improvement of 1.29 cm over that measured by a resident surgeon (P < 0.001). In addition, The results measured by the resident surgeon referring to our segmentation results improved by 1.46 cm compared to the results measured independently by the residents. As experimentally demonstrated, our method exhibits great application potential in clinical scenarios.

RMAU-Net: Residual Multi-Scale Attention U-Net For liver and tumor segmentation in CT images

Liver cancer is one of the leading causes of cancer-related deaths worldwide. Automatic liver and tumor segmentation are of great value in clinical practice as they can reduce surgeons' workload and increase the probability of success in surgery. Liver and tumor segmentation is a challenging task because of the different sizes, shapes, blurred boundaries of livers and lesions, and low-intensity contrast between organs within patients. To address the problem of fuzzy livers and small tumors, we propose a novel Residual Multi-scale Attention U-Net (RMAU-Net) for liver and tumor segmentation by introducing two modules, i.e., Res-SE-Block and MAB. The Res-SE-Block can mitigate the problem of gradient disappearance by residual connection and enhance the quality of representations by explicitly modeling the interdependencies and feature recalibration between the channels of features. The MAB can exploit rich multi-scale feature information and capture inter-channel and inter-spatial relationships of features simultaneously. In addition, a hybrid loss function, that combines focal loss and dice loss, is designed to improve segmentation accuracy and speed up convergence. We evaluated the proposed method on two publicly available datasets, i.e., LiTS and 3D-IRCADb. Our proposed method achieved better performance than the other state-of-the-art methods, with dice scores of 0.9552 and 0.9697 for LiTS and 3D-IRCABb liver segmentation, and dice scores of 0.7616 and 0.8307 for LiTS and 3D-IRCABb liver tumor segmentation.

An omics-to-omics joint knowledge association subtensor model for radiogenomics cross-modal modules from genomics and ultrasonic images of breast cancers

The radiogenomics analysis can provide the connections between genomics and radiomics, which can infer the genomic features of tumors from their radiogenomic associations through the low-cost and non-invasiveness screening ultrasonic images. Although there are a number of pioneer approaches exploring the connections between genomic aberrations and ultrasonic features, these studies mainly focus on the relationship between ultrasonic features and only the most popular cancer genes, confronting two difficulties: missing many-to-many relationships as omics-to-omics view, and confounding group-specific associations with whole sample associations. To overcome the difficulty of omics-to-omics view and the issue of tumor heterogeneity, we propose an omics-to-omics joint knowledge association subtensor model. Specifically, the subtensor factorization framework can successfully discover the joint cross-modal module via an omics-to-omics view, while the sparse weight sample indication strategy can mine sample subgroups from the multi-omic data with tumor heterogeneity. The experimental evaluation result shows the jointness of the discovered modules across omics, their association with tumorigenesis contribution, and their relation for cancer related functions. In summary, our proposed omics-to-omics joint knowledge association subtensor model can serve as an efficient tool for radiogenomic knowledge associations, promoting the cross-modal knowledge graph construction of in explainable artificial intelligence cancer diagnosis.