Cross-Cohort Automatic Knee MRI Segmentation With Multi-Planar U-Nets

Three-Direction Fusion for Accurate Volumetric Liver and Tumor Segmentation

Biomedical image segmentation of organs, tissues and lesions has gained increasing attention in clinical treatment planning and navigation, which involves the exploration of two-dimensional (2D) and three-dimensional (3D) contexts in the biomedical image. Compared to 2D methods, 3D methods pay more attention to inter-slice correlations, which offer additional spatial information for image segmentation. An organ or tumor has a 3D structure that can be observed from three directions. Previous studies focus only on the vertical axis, limiting the understanding of the relationship between a tumor and its surrounding tissues. Important information can also be obtained from sagittal and coronal axes. Therefore, spatial information of organs and tumors can be obtained from three directions, i.e. the sagittal, coronal and vertical axes, to understand better the invasion depth of tumor and its relationship with the surrounding tissues. Moreover, the edges of organs and tumors in biomedical image may be blurred. To address these problems, we propose a three-direction fusion volumetric segmentation (TFVS) model for segmenting 3D biomedical images from three perspectives in sagittal, coronal and transverse planes, respectively. We use the dataset of the liver task provided by the Medical Segmentation Decathlon challenge to train our model. The TFVS method demonstrates a competitive performance on the 3D-IRCADB dataset. In addition, the t-test and Wilcoxon signed-rank test are also performed to show the statistical significance of the improvement by the proposed method as compared with the baseline methods. The proposed method is expected to be beneficial in guiding and facilitating clinical diagnosis and treatment.

Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022

Objective

The aim of this literature review is to yield a comprehensive and exhaustive overview of the existing evidence and up-to-date applications of artificial intelligence for knee osteoarthritis.

Methods

A literature review was performed by using PubMed, Google Scholar, and IEEE databases for articles published in peer-reviewed journals in 2022. The articles focusing on the use of artificial intelligence in diagnosis and prognosis of knee osteoarthritis and accelerating the image acquisition were selected. For each selected study, the code availability, considered number of patients and knees, imaging type, covariates, grading type of osteoarthritis, models, validation approaches, objectives, and results were reviewed.

Results

395 articles were screened, and 35 of them were reviewed. Eight articles were based on diagnosis, six on prognosis prediction, three on classification, three on accelerated image acquisition, and 15 on segmentation of knee osteoarthritis. 57% of the articles used MRI, 26% radiography, 6% MRI together with radiography, 6% ultrasonography, and 6% only clinical data. 23% of the articles made the computer codes available for their study, and 26% used clinical data. External validation and nested cross-validation were used in 17% and 14% of articles, respectively.

Conclusions

The use of artificial intelligence provided a promising potential to enhance the detection and management of knee osteoarthritis. Translating the developed models into clinics is still in the early stages of development. The translation of artificial intelligence models is expected to be further examined in prospective studies to support clinicians in improving routine healthcare practice.

Age-Related Changes in the Spatial Variation of Magnetic Susceptibility of Human Articular Cartilage

BACKGROUND

Quantitative susceptibility mapping (QSM) has shown great potential for revealing the layer structure of articular cartilage based on the laminar susceptibility difference at different depths. However, more information is needed on the effects of age on the spatial distribution of magnetic susceptibility in human cartilage.

PURPOSE

To assess the ability of QSM to quantify the age-related differences in depth-wise cartilage susceptibility values in healthy populations.

STUDY TYPE

Prospective.

POPULATION

A total of 94 healthy asymptomatic subjects in three age cohorts: 19-30 (n = 36, 20 males), 31-50 (n = 45, 27 males), and 51-66 years (n = 13, 7 males).

FIELD STRENGTH/SEQUENCE

3D gradient echo sequences at 3.0 T.

ASSESSMENT

Four cartilage compartments were analyzed, including the central lateral/medial femur (cLF/cMF) and the lateral/medial tibia (LT/MT). The spatial susceptibility profile and the corresponding 95% confidence interval (CI) of each age cohort were obtained as functions of the normalized distance from the bone-cartilage interface to the cartilage surface (cartilage depth from 0.0 to 1.0).

STATISTICAL TESTS

The relationship between age and cartilage thickness of each cartilage subregion was tested by Pearson correlation with P < 0.05 considered significant. Cartilage depths with separations of 95% CIs were considered to have significant susceptibility differences between two age cohorts with a Bonferroni-corrected P < 0.05.

RESULTS

The cartilage thickness did not change significantly with age (P value range: 0.06-0.85). Susceptibilities were significantly higher in the 51-66-year-olds compared with the 31-50-year-olds in the deep layer of cMF (cartilage depth: 0.0-0.22) and LT (0.05-0.28). Susceptibilities were significantly higher in the 51-66-year-olds compared with the 19-30-year-olds near the cartilage-bone interface of cMF (0.0-0.34), cLF (0.0-0.28), and LT (0.0-0.58). There were also significantly higher susceptibilities in the 31-50-year-olds compared with the 19-30-year-olds in the deeper regions of cMF (0.26-0.57), cLF (0.0-0.40), and LT (0.07-0.80).

DATA CONCLUSION

Age-related susceptibility changes in the deeper regions of knee cartilage were observed using QSM.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Near-Infrared Blood Vessel Image Segmentation Using Background Subtraction and Improved Mathematical Morphology

The precise display of blood vessel information for doctors is crucial. This is not only true for facilitating intravenous injections, but also for the diagnosis and analysis of diseases. Currently, infrared cameras can be used to capture images of superficial blood vessels. However, their imaging quality always has the problems of noises, breaks, and uneven vascular information. In order to overcome these problems, this paper proposes an image segmentation algorithm based on the background subtraction and improved mathematical morphology. The algorithm regards the image as a superposition of blood vessels into the background, removes the noise by calculating the size of connected domains, achieves uniform blood vessel width, and smooths edges that reflect the actual blood vessel state. The algorithm is evaluated subjectively and objectively in this paper to provide a basis for vascular image quality assessment. Extensive experimental results demonstrate that the proposed method can effectively extract accurate and clear vascular information.

Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning

We systematically evaluate the training methodology and efficacy of two inpainting-based pretext tasks of context prediction and context restoration for medical image segmentation using self-supervised learning (SSL). Multiple versions of self-supervised U-Net models were trained to segment MRI and CT datasets, each using a different combination of design choices and pretext tasks to determine the effect of these design choices on segmentation performance. The optimal design choices were used to train SSL models that were then compared with baseline supervised models for computing clinically-relevant metrics in label-limited scenarios. We observed that SSL pretraining with context restoration using 32 × 32 patches and Poission-disc sampling, transferring only the pretrained encoder weights, and fine-tuning immediately with an initial learning rate of 1 × 10-3 provided the most benefit over supervised learning for MRI and CT tissue segmentation accuracy (p < 0.001). For both datasets and most label-limited scenarios, scaling the size of unlabeled pretraining data resulted in improved segmentation performance. SSL models pretrained with this amount of data outperformed baseline supervised models in the computation of clinically-relevant metrics, especially when the performance of supervised learning was low. Our results demonstrate that SSL pretraining using inpainting-based pretext tasks can help increase the robustness of models in label-limited scenarios and reduce worst-case errors that occur with supervised learning.

Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies

By leveraging the recent development of artificial intelligence algorithms, several medical sectors have benefited from using automatic segmentation tools from bioimaging to segment anatomical structures. Segmentation of the musculoskeletal system is key for studying alterations in anatomical tissue and supporting medical interventions. The clinical use of such tools requires an understanding of the proper method for interpreting data and evaluating their performance. The current systematic review aims to present the common bottlenecks for musculoskeletal structures analysis (e.g., small sample size, data inhomogeneity) and the related strategies utilized by different authors. A search was performed using the PUBMED database with the following keywords: deep learning, musculoskeletal system, segmentation. A total of 140 articles published up until February 2022 were obtained and analyzed according to the PRISMA framework in terms of anatomical structures, bioimaging techniques, pre/post-processing operations, training/validation/testing subset creation, network architecture, loss functions, performance indicators and so on. Several common trends emerged from this survey; however, the different methods need to be compared and discussed based on each specific case study (anatomical region, medical imaging acquisition setting, study population, etc.). These findings can be used to guide clinicians (as end users) to better understand the potential benefits and limitations of these tools.