Journal of Medical Imaging最新文献

Purpose: The purpose is to retrospectively investigate how the addition of prior and concurrent mammograms affects wide-angle digital breast tomosynthesis (DBT) screening false-positive recall rates, malignancy scoring, and recall agreement.

Approach: A total of 200 cases were selected from the Malmö Breast Tomosynthesis Screening Trial. They consist of 150 recalled cases [30 true positives (TPs), 120 false positives (FPs), and 50 healthy, non-recalled true-negative (TN) cases]. The positive cases were categorized based on being recalled by either DBT, digital mammography (DM), or both. Each case had DBT, synthetic mammography (SM), and DM (prior screening round) images. Five radiologists participated in a reading study where detection, risk of malignancy, and recall were assessed. They read each case twice, once using only DBT and once using DBT together with SM and DM priors.

Results: The results showed a significant reduction in recall rates for all FP categories, as well as for the TN cases, when adding SM and prior DM to DBT. This resulted also in a significant increase in recall agreement for these categories, with more of the negative cases being recalled by few or no readers. These categories were overall rated as appearing more malignant in the DBT reading arm. For the TP categories, there was a significant decrease in recalls for DM-recalled cancers ( $p=0.047$ ), but no significant difference for DBT-recalled cancers ( $p=0.063$ ), or DBT/DM-recalled cancers ( $p=0.208$ ).

Conclusions: Similar to the documented effect of priors in DM screening, we suggest that added two-dimensional priors improve the specificity of DBT screening but may reduce the sensitivity.

Purpose: Breast cancer is one of the leading causes of cancer-related deaths among women, and digital mammography plays a key role in screening and early detection. The radiation dose on mammographic exams directly influences image quality and radiologists' performance. We evaluate the impact of an image restoration pipeline-designed to simulate higher dose acquisitions-on the detectability of microcalcifications of various sizes in mammograms acquired at different radiation doses.

Approach: The restoration pipeline denoises the image using a Poisson-Gaussian noise model, combining it with the noisy image to achieve a signal-to-noise ratio comparable with an acquisition at twice the original dose. We created a database of images using a physical breast phantom at doses ranging from 50% to 200% of the standard dose. Clustered microcalcifications were computationally inserted into the phantom images. The channelized Hotelling observer was employed in a four-alternative forced-choice to evaluate the detectability of microcalcifications across different sizes and exposure levels.

Results: The restoration of low-dose images acquired at $\sim 75\%$ of the standard dose resulted in detectability levels comparable with those of images acquired at the standard dose. Moreover, images restored at the standard dose demonstrated detectability similar to those acquired at 160% of the nominal radiation dose, with no statistically significant differences.

Conclusions: We demonstrate the potential of an image restoration pipeline to simulate higher quality mammography images. The results indicate that reducing noise through denoising and restoration impacts the detectability of microcalcifications. This method improves image quality without hardware modifications or additional radiation exposure.

Purpose: Virtual imaging trials (VITs) are of interest for regulatory evaluation because they enable faster and more cost-effective evaluation of new imaging technologies than patient clinical trials. Our purpose is to develop a hybrid VIT methodology for breast computed tomography (CT) applications and use it to investigate microcalcification detectability.

Approach: Ray tracing was used to generate projection images of clusters of five microcalcifications which varied in diameter, chemical composition, and density. These simulated projection images were added to patient projection images acquired with the fourth-generation breast CT scanner from UC Davis (Doheny) and reconstructed using the Feldkamp filtered backprojection algorithm with varying apodization kernels. Volumes of interest and maximum intensity projections were extracted from the reconstructed volumes. Human observers (HOs) and deep learning model observers (DLMOs) were used to detect calcification clusters, and receiver operating characteristic curve analysis was used to analyze detection performance.

Results: DLMO detected 0.18-mm type I calcifications with AUC = 0.80 and 0.21 mm calcifications with $\mathrm{AUC}=0.99$ . HO performance was inferior to deep learning model observer performance, but both HO and DLMO detected 0.21-mm type I calcifications with $\mathrm{AUC}>0.90$ and 0.24-mm type I calcifications with near-perfect performance. Microcalcification clusters embedded in adipose tissue were more conspicuous than clusters embedded in fibroglandular tissue. There was superior detection performance for clusters located anteriorly within the breast compared with clusters located posteriorly within the breast.

Conclusions: A hybrid approach for virtual imaging trials shows promise for the assessment of imaging systems across a broad range of parameters.

Purpose: Data-driven harmonization can mitigate systematic confounding signals across imaging cohorts caused by variance in scanners and acquisition protocols. As diffusion magnetic resonance imaging data are often acquired with different hardware and software, harmonization is essential for integrating these scattered datasets into a cohesive analysis for improved statistical power. Large-scale, multi-site studies for Alzheimer's disease (AD), a neurodegenerative condition characterized by high data variability and complex pathology, pose the challenge of both site-based and biological variation.

Approach: We learn lower-dimensional representations of structural connectivity invariant to imaging cohort, geographical location, scanner, and acquisition factors. We design a conditional variational autoencoder that creates latent representations with minimal information about imaging factors and maximal information related to patient cognitive status. With this model, we consolidate 9 cohorts and 35 unique imaging acquisitions (for a total of 38 imaging "sites") into a cohesive dataset of 6956 persons (16.4% with mild cognitive impairment and 10.7% with AD) imaged for 1 to 16 sessions for a total of 11,927 diffusion-weighted imaging sessions.

Results: These site-invariant representations successfully remove significant ( $p<0.05$ ) site effects in 12 network connectivity measures of interest and enhance the prediction of cognitive diagnosis (from 68% accuracy to 73% accuracy).

Conclusions: The proposed model yields reproducible precision across 15 data configurations. This approach demonstrates the effectiveness of representation learning in enhancing biological signals by mitigating acquisition-specific confounding factors in neuroimaging studies.

Purpose: Accurate segmentation and precise delineation of colorectal polyp structures are crucial for early clinical diagnosis and treatment planning. However, existing polyp segmentation techniques face significant challenges due to the high variability in polyp size and morphology, as well as the frequent indistinctness of polyp-tissue structures.

Approach: To address these challenges, we propose a multiscale attention network with structure guidance (MAN-SG). The core of MAN-SG is a structure extraction module (SEM) designed to capture rich structural information from fine-grained early-stage encoder features. In addition, we introduce a cross-scale structure guided attention (CSGA) module that effectively fuses multiscale features under the guidance of the structural information provided by the SEM, thereby enabling more accurate delineation of polyp structures. MAN-SG is implemented and evaluated using two high-performance backbone networks: Res2Net-50 and PVTv2-B2.

Results: Extensive experiments were conducted on five benchmark datasets for polyp segmentation. The results demonstrate that MAN-SG consistently outperforms existing state-of-the-art methods across these datasets.

Conclusion: The proposed MAN-SG framework, which leverages structural guidance via SEM and CSGA modules, proves to be both highly effective and robust for the challenging task of colorectal polyp segmentation.

Purpose: Deep learning (DL) is rapidly advancing in computational pathology, offering high diagnostic accuracy but often functioning as a "black box" with limited interpretability. This lack of transparency hinders its clinical adoption, emphasizing the need for quantitative explainable artificial intelligence (QXAI) methods. We propose a QXAI approach to objectively and quantitatively elucidate the reasoning behind DL model decisions in hepatocellular carcinoma (HCC) pathological image analysis.

Approach: The proposed method utilizes clustering in the latent space of embeddings generated by a DL model to identify regions that contribute to the model's discrimination. Each cluster is then quantitatively characterized by morphometric features obtained through nuclear segmentation using HoverNet and key feature selection with LightGBM. Statistical analysis is performed to assess the importance of selected features, ensuring an interpretable relationship between morphological characteristics and classification outcomes. This approach enables the quantitative interpretation of which regions and features are critical for the model's decision-making, without sacrificing accuracy.

Results: Experiments on pathology images of hematoxylin-and-eosin-stained HCC tissue sections showed that the proposed method effectively identified key discriminatory regions and features, such as nuclear size, chromatin density, and shape irregularity. The clustering-based analysis provided structured insights into morphological patterns influencing classification, with explanations evaluated as clinically relevant and interpretable by a pathologist.

Conclusions: Our QXAI framework enhances the interpretability of DL-based pathology analysis by linking morphological features to classification decisions. This fosters trust in DL models and facilitates their clinical integration.

Purpose: Segmenting intraglomerular tissue and glomerular lesions traditionally depends on detailed morphological evaluations by expert nephropathologists, a labor-intensive process susceptible to interobserver variability. Our group previously developed the Glo-In-One toolkit for integrated glomerulus detection and segmentation. We leverage the Glo-In-One toolkit to version 2 (Glo-In-One-v2), which adds fine-grained segmentation capabilities. We curated 14 distinct labels spanning tissue regions, cells, and lesions across 23,529 annotated glomeruli from human and mouse histopathology data. To our knowledge, this dataset is among the largest of its kind to date.

Approach: We present a single dynamic-head deep learning architecture for segmenting 14 classes within partially labeled images from human and mouse kidney pathology. The model was trained on data derived from 368 annotated kidney whole-slide images with five key intraglomerular tissue types and nine glomerular lesion types.

Results: The glomerulus segmentation model achieved a decent performance compared with baselines and achieved a 76.5% average Dice similarity coefficient. In addition, transfer learning from rodent to human for the glomerular lesion segmentation model has enhanced the average segmentation accuracy across different types of lesions by more than 3%, as measured by Dice scores.

Conclusions: We introduce a convolutional neural network for multiclass segmentation of intraglomerular tissue and lesions. The Glo-In-One-v2 model and pretrained weight are publicly available at https://github.com/hrlblab/Glo-In-One_v2.

Purpose: To achieve the high sensitivity of digital breast tomosynthesis (DBT), a time-consuming reading is necessary. However, synthetic mammography (SM) images, equivalent to digital mammography (DM), can be generated from DBT images. SM is faster to read and might be sufficient in many cases. We investigate using artificial intelligence (AI) to stratify examinations into reading of either SM or DBT to minimize workload and maximize accuracy.

Approach: This is a retrospective study based on double-read paired DM and one-view DBT from the Malmö Breast Tomosynthesis Screening Trial. DBT examinations were analyzed with the cancer detection AI system ScreenPoint Transpara 1.7. For low-risk examinations, SM reading was simulated by assuming equality with DM reading. For high-risk examinations, the DBT reading results were used. Different combinations of single and double reading were studied.

Results: By double-reading the DBT of 30% (4452/14,772) of the cases with the highest risk, and single-reading SM for the rest, 122 cancers would be detected with the same reading workload as DM double reading. That is 28% (27/95) more cancers would be detected than with DM double reading, and in total, 96% (122/127) of the cancers detectable with full DBT double reading would be found.

Conclusions: In a DBT-based screening program, AI could be used to select high-risk cases where the reading of DBT is valuable, whereas SM is sufficient for low-risk cases. Substantially, more cancers could be detected compared with DM only, with only a limited increase in reading workload. Prospective studies are necessary.

Purpose: Breast density estimation is an important part of breast cancer risk assessment, as mammographic density is associated with risk. However, density assessed by multiple experts can be subject to high inter-observer variability, so automated methods are increasingly used. We investigate the inter-reader variability and risk prediction for expert assessors and a deep learning approach.

Approach: Screening data from a cohort of 1328 women, case-control matched, was used to compare between two expert readers and between a single reader and a deep learning model, Manchester artificial intelligence - visual analog scale (MAI-VAS). Bland-Altman analysis was used to assess the variability and matched concordance index to assess risk.

Results: Although the mean differences for the two experiments were alike, the limits of agreement between MAI-VAS and a single reader are substantially lower at +SD (standard deviation) 21 (95% CI: 19.65, 21.69) -SD 22 (95% CI: $-22.71$ , $-20.68$ ) than between two expert readers +SD 31 (95% CI: 32.08, 29.23) -SD 29 (95% CI: $-29.94$ , $-27.09$ ). In addition, breast cancer risk discrimination for the deep learning method and density readings from a single expert was similar, with a matched concordance of 0.628 (95% CI: 0.598, 0.658) and 0.624 (95% CI: 0.595, 0.654), respectively. The automatic method had a similar inter-view agreement to experts and maintained consistency across density quartiles.

Conclusions: The artificial intelligence breast density assessment tool MAI-VAS has a better inter-observer agreement with a randomly selected expert reader than that between two expert readers. Deep learning-based density methods provide consistent density scores without compromising on breast cancer risk discrimination.