Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is considered the in vivo reference standard for assessing infarct size (IS) and microvascular obstruction (MVO) in ST-elevation myocardial infarction (STEMI) patients. However, the exact quantification of those markers of myocardial infarct severity remains challenging and very time-consuming. As LGE distribution patterns can be quite complex and hard to delineate from the blood pool or epicardial fat, automatic segmentation of LGE CMR images is challenging. In this work, we propose a cascaded framework of two-dimensional and three-dimensional convolutional neural networks (CNNs) which enables to calculate the extent of myocardial infarction in a fully automated way. By artificially generating segmentation errors which are characteristic for 2D CNNs during training of the cascaded framework we are enforcing the detection and correction of 2D segmentation errors and hence improve the segmentation accuracy of the entire method. The proposed method was trained and evaluated on two publicly available datasets. We perform comparative experiments where we show that our framework outperforms state-of-the-art reference methods in segmentation of myocardial infarction. Furthermore, in extensive ablation studies we show the advantages that come with the proposed error correcting cascaded method. The code of this project is publicly available at https://github.com/matthi99/EcorC.git.

Deep learning has revolutionized medical image segmentation, yet its full potential remains constrained by the paucity of annotated datasets. While diffusion models have emerged as a promising approach for generating synthetic image-mask pairs to augment these datasets, they paradoxically suffer from the same data scarcity challenges they aim to mitigate. Traditional mask-only models frequently yield low-fidelity images due to their inability to adequately capture morphological intricacies, which can critically compromise the robustness and reliability of segmentation models. To alleviate this limitation, we introduce Siamese-Diffusion, a novel dual-component model comprising Mask-Diffusion and Image-Diffusion. During training, a Noise Consistency Loss is introduced between these components to enhance the morphological fidelity of Mask-Diffusion in the parameter space. During sampling, only Mask-Diffusion is used, ensuring diversity and scalability. Comprehensive experiments demonstrate the superiority of our method. Siamese-Diffusion boosts SANet's mDice and mIoU by 3.6% and 4.4% on the Polyps, while UNet improves by 1.52% and 1.64% on the ISIC2018. Code is available at GitHub.

The distribution of visceral adipose tissue (VAT) in cystectomy patients is indicative of the incidence of postoperative complications. Existing VAT segmentation methods for computed tomography (CT) employing intensity thresholding have limitations relating to inter-observer variability. Moreover, the difficulty in creating ground-truth masks limits the development of deep learning (DL) models for this task. This paper introduces a novel method for VAT prediction in pre-cystectomy CT, which is fully automated and does not require ground-truth VAT masks for training, overcoming aforementioned limitations. We introduce the kernel density-enhanced VAT segmentator (KEVS), combining a DL semantic segmentation model, for multi-body feature prediction, with Gaussian kernel density estimation analysis of predicted subcutaneous adipose tissue to achieve accurate scan-specific predictions of VAT in the abdominal cavity. Uniquely for a DL pipeline, KEVS does not require ground-truth VAT masks. We verify the ability of KEVS to accurately segment abdominal organs in unseen CT data and compare KEVS VAT segmentation predictions to existing state-of-the-art (SOTA) approaches in a dataset of 20 pre-cystectomy CT scans, collected from University College London Hospital (UCLH-Cyst), with expert ground-truth annotations. KEVS presents a <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>4.80</mn> <mo>%</mo></mrow> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>6.02</mn> <mo>%</mo></mrow> </math> improvement in Dice coefficient over the second best DL and thresholding-based VAT segmentation techniques respectively when evaluated on UCLH-Cyst. This research introduces KEVS, an automated, SOTA method for the prediction of VAT in pre-cystectomy CT which eliminates inter-observer variability and is trained entirely on open-source CT datasets which do not contain ground-truth VAT masks.

Current methods for medical image segmentation primarily focus on extracting contextual feature information from the perspective of the whole image. While these methods have shown effective performance, none of them take into account the fact that pixels at the boundary and regions with a low number of class pixels capture more contextual feature information from other classes, leading to misclassification of pixels by unequal contextual feature information. In this paper, we propose a dual feature equalization network based on the hybrid architecture of Swin Transformer and Convolutional Neural Network, aiming to augment the pixel feature representations by image-level equalization feature information and class-level equalization feature information. Firstly, the image-level feature equalization module is designed to equalize the contextual information of pixels within the image. Secondly, we aggregate regions of the same class to equalize the pixel feature representations of the corresponding class by class-level feature equalization module. Finally, the pixel feature representations are enhanced by learning weights for image-level equalization feature information and class-level equalization feature information. In addition, Swin Transformer is utilized as both the encoder and decoder, thereby bolstering the ability of the model to capture long-range dependencies and spatial correlations. We conducted extensive experiments on Breast Ultrasound Images (BUSI), International Skin Imaging Collaboration (ISIC2017), Automated Cardiac Diagnosis Challenge (ACDC) and PH$^2$ datasets. The experimental results demonstrate that our method have achieved state-of-the-art performance. Our code is publicly available at https://github.com/JianJianYin/DFEN.

Automatic lymph node segmentation is the cornerstone for advances in computer vision tasks for early detection and staging of cancer. Traditional segmentation methods are constrained by manual delineation and variability in operator proficiency, limiting their ability to achieve high accuracy. The introduction of deep learning technologies offers new possibilities for improving the accuracy of lymph node image analysis. This study evaluates the application of deep learning in lymph node segmentation and discusses the methodologies of various deep learning architectures such as convolutional neural networks, encoder-decoder networks, and transformers in analyzing medical imaging data across different modalities. Despite the advancements, it still confronts challenges like the shape diversity of lymph nodes, the scarcity of accurately labeled datasets, and the inadequate development of methods that are robust and generalizable across different imaging modalities. To the best of our knowledge, this is the first study that provides a comprehensive overview of the application of deep learning techniques in lymph node segmentation task. Furthermore, this study also explores potential future research directions, including multimodal fusion techniques, transfer learning, and the use of large-scale pre-trained models to overcome current limitations while enhancing cancer diagnosis and treatment planning strategies.

Pancreatic ductal adenocarcinoma (PDA) is an extremely metastatic and lethal disease. In PDA, extracellular matrix (ECM) architectures known as Tumor-Associated Collagen Signatures (TACS) regulate invasion and metastatic spread in both early dissemination and in late-stage disease. As such, TACS has been suggested as a biomarker to aid in pathologic assessment. However, despite its significance, approaches to quantitatively capture these ECM patterns currently require advanced optical systems with signaling processing analysis. Here we present an expansion of polychromatic polarized microscopy (PPM) with inherent angular information coupled to machine learning and computational pixel-wise analysis of TACS. Using this platform, we are able to accurately capture TACS architectures in H&E stained histology sections directly through PPM contrast. Moreover, PPM facilitated identification of transitions to dissemination architectures, i.e., transitions from sequestration through expansion to dissemination from both PanINs and throughout PDA. Lastly, PPM evaluation of architectures in liver metastases, the most common metastatic site for PDA, demonstrates TACS-mediated focal and local invasion as well as identification of unique patterns anchoring aligned fibers into normal-adjacent tumor, suggesting that these patterns may be precursors to metastasis expansion and local spread from micrometastatic lesions. Combined, these findings demonstrate that PPM coupled to computational platforms is a powerful tool for analyzing ECM architecture that can be employed to advance cancer microenvironment studies and provide clinically relevant diagnostic information.

 Fibrotic lung disease is a progressive illness that causes scarring and ultimately respiratory failure, with irreversible damage by the time it is diagnosed on computed tomography imaging. Recent research postulates the role of the lung vasculature on the pathogenesis of the disease. With the recent development of high-resolution hierarchical phase-contrast tomography (HiP-CT), we have the potential to understand and detect changes in the lungs long before conventional imaging. However, to gain quantitative insight into vascular changes you first need to be able to segment the vessels before further downstream analysis can be conducted. Aside from this, HiP-CT generates large-volume, high-resolution data which is time-consuming and expensive to label.  This project aims to qualitatively assess the latest machine learning methods for vessel segmentation in HiP-CT data to enable label propagation as the first step for imaging biomarker discovery, with the goal to identify early-stage interstitial lung disease amenable to treatment, before fibrosis begins.  Semisupervised learning (SSL) has become a growing method to tackle sparsely labeled datasets due to its leveraging of unlabeled data. In this study, we will compare two SSL methods; Seg PL, based on pseudo-labeling, and MisMatch, using consistency regularization against state-of-the-art supervised learning method, nnU-Net, on vessel segmentation in sparsely labeled lung HiP-CT data.  On initial experimentation, both MisMatch and SegPL showed promising performance on qualitative review. In comparison with supervised learning, both MisMatch and SegPL showed better out-of-distribution performance within the same sample (different vessel morphology and texture vessels), though supervised learning provided more consistent segmentations for well-represented labels in the limited annotations.  Further quantitative research is required to better assess the generalizability of these findings, though they show promising first steps toward leveraging this novel data to tackle fibrotic lung disease.

Magnetic resonance imaging (MRI) has the potential to identify post-operative risk factors for re-tearing an anterior cruciate ligament (ACL) using a combination of imaging signal intensity (SI) and cross-sectional area measurements of the healing ACL. During surgery micro-debris can result from drilling the osseous tunnels for graft and/or suture insertion. The debris presents a limitation when using post-surgical MRI to assess reinjury risk as it causes rapid magnetic field variations during acquisition, leading to signal loss within a voxel. The present study demonstrates how K-means clustering can refine an automatic segmentation algorithm to remove the lost signal intensity values induced by the artifacts in the image. MRI data were obtained from 82 patients enrolled in three prospective clinical trials of ACL surgery. Constructive Interference in Steady State MRIs were collected at 6 months post-operation. Manual segmentation of the ACL with metallic artifacts removed served as the gold standard. The accuracy of the automatic ACL segmentations was compared using Dice coefficient, sensitivity, and precision. The performance of the automatic segmentation was comparable to manual segmentation (Dice coefficient = .81, precision = .81, sensitivity = .82). The normalized average signal intensity was calculated as 1.06 (±0.25) for the automatic and 1.04 (±0.23) for the manual segmentation, yielding a difference of 2%. These metrics emphasize the automatic segmentation model's ability to precisely capture ACL signal intensity while excluding artifact regions. The automatic artifact segmentation model described here could enhance qMRI's clinical utility by allowing for more accurate and time-efficient segmentations of the ACL.

Online adaptive magnetic resonance-guided radiotherapy (MRgRT) has emerged as a state-of-the-art treatment option for multiple tumour entities, accounting for daily anatomical and tumour volume changes, thus allowing sparing of relevant organs at risk (OARs). However, the annotation of treatment-relevant anatomical structures in context of online plan adaptation remains challenging, often relying on commercial segmentation solutions due to limited availability of clinically validated alternatives. The aim of this study was to investigate whether an open-source artificial intelligence (AI) segmentation network can compete with the annotation accuracy of a commercial solution, both trained on the identical dataset, questioning the need for commercial models in clinical practice. For 47 pelvic patients, T2w MR imaging data acquired on a 1.5 T MR-Linac were manually contoured, identifying prostate, seminal vesicles, rectum, anal canal, bladder, penile bulb, and bony structures. These training data were used for the generation of an in-house AI segmentation model, a nnU-Net with residual encoder architecture featuring a streamlined single image inference pipeline, and re-training of a commercial solution. For quantitative evaluation, 20 MR images were contoured by a radiation oncologist, considered as ground truth contours (GTC) and compared with the in-house/commercial AI-based contours (iAIC/cAIC) using Dice Similarity Coefficient (DSC), 95% Hausdorff distances (HD95), and surface DSC (sDSC). For qualitative evaluation, four radiation oncologists assessed the usability of OAR/target iAIC within an online adaptive workflow using a four-point Likert scale: (1) acceptable without modification, (2) requiring minor adjustments, (3) requiring major adjustments, and (4) not usable. Patient-individual annotations were generated in a median [range] time of 23 [16-34] s for iAIC and 152 [121-198] s for cAIC, respectively. OARs showed a maximum median DSC of 0.97/0.97 (iAIC/cAIC) for bladder and minimum median DSC of 0.78/0.79 (iAIC/cAIC) for anal canal/penile bulb. Maximal respectively minimal median HD95 were detected for rectum with 17.3/20.6 mm (iAIC/cAIC) and for bladder with 5.6/6.0 mm (iAIC/cAIC). Overall, the average median DSC/HD95 values were 0.87/11.8mm (iAIC) and 0.83/10.2mm (cAIC) for OAR/targets and 0.90/11.9mm (iAIC) and 0.91/16.5mm (cAIC) for bony structures. For a tolerance of 3 mm, the highest and lowest sDSC were determined for bladder (iAIC:1.00, cAIC:0.99) and prostate in iAIC (0.89) and anal canal in cAIC (0.80), respectively. Qualitatively, 84.8% of analysed contours were considered as clinically acceptable for iAIC, while 12.9% required minor and 2.3% major adjustments or were classed as unusable. Contour-specific analysis showed that iAIC achieved the highest mean scores with 1.00 for the anal canal and the lowest with 1.61 for the prostate. This study demonstrates that open-source segmentation framework can achieve comparable annotation accuracy to commercial solutions for pelvic anatomy in online adaptive MRgRT. The adapted framework not only maintained high segmentation performance, with 84.8% of contours accepted by physicians or requiring only minor corrections (12.9%) but also enhanced clinical workflow efficiency of online adaptive MRgRT through reduced inference times. These findings establish open-source frameworks as viable alternatives to commercial systems in supervised clinical workflows.

Radiologists' eye movements during medical image interpretation reflect their perceptual-cognitive processes of diagnostic decisions. The eye movement data can be modeled to represent clinically relevant regions in a medical image and potentially integrated into an artificial intelligence (AI) system for automatic diagnosis in medical imaging. In this article, we first conduct a large-scale eye-tracking study involving 13 radiologists interpreting 191 chest X-ray (CXR) images, establishing a best-of-its-kind CXR visual saliency benchmark. We then perform analysis to quantify the reliability and clinical relevance of saliency maps (SMs) generated for CXR images. We develop CXR image saliency prediction method (CXRSalNet), a novel saliency prediction model that leverages radiologists' gaze information to optimize the use of unlabeled CXR images, enhancing training and mitigating data scarcity. We also demonstrate the application of our CXR saliency model in enhancing the performance of AI-powered diagnostic imaging systems.