Precision surgical interventions rely on accurate integration of preoperative and intraoperative imaging to guide clinical decision-making and improve patient outcomes. Traditionally, most 3D imaging modalities capture the entirety of the target object, allowing for the segmentation of its entire volume. However, some medical imaging devices trade full field-of-view for other considerations, such as size and ability to access the target organ in unique ways. This is particularly true of ultrasound, where imaging deeper organs from outside the body is not viable due to attenuation, thus motivating the need for alternative imaging strategies. In scenarios such as image-guided intervention, it is often necessary to put this partial view of the object in its full anatomical context, such as alignment to a pre-operative MRI. We propose a method for jointly segmenting the portion of the object visible in the acquired image and estimating its full shape. We do this by combining a pre-trained shape prior with a patient-specific expectation of organ shape acquired via pre-operative imaging. In a simulated dataset for prostate MRI/US fusion, we show the ability to accurately estimate the prostate shape from ultrasound images capturing only a fraction of its total volume.

PURPOSE OF REVIEW The degree to which computerized methods, such as artificial intelligence (AI), will aid in the assessment of kidney histopathology is undergoing intense study and application; and this is particularly true for interstitial fibrosis, which is often used as a surrogate measure of chronic kidney disease progression, since interobserver variability among human pathologists has been demonstrated in the assessment of interstitial fibrosis and other features. RECENT FINDINGS Computerized assessment of interstitial fibrosis, including with AI, has been assessed alongside pathologists. Computerized methods such as AI have shown direct interstitial fibrosis measurement and indirect assessment through kidney compartment segmentation; however, some studies have shown lack of complete concordance among computerized methods and humans; and studies have still shown the persistent value of human assessment in many circumstances. SUMMARY Computerized methods, including AI, are showing increased application in kidney pathology for a wide variety of clinical and histopathologic parameter assessment, including interstitial fibrosis; however, further studies are needed to characterize the performance of AI and handcrafted methods; and additional work is needed to fully integrate computerized methods into routine pathology practice. Ultimately, humans working with AI ("humans + AI") may provide enhanced analysis for more effective patient care.

Abstract The Open and Reproducible Musculoskeletal Imaging Research community is a scientific community dedicated to promoting openness and reproducibility in musculoskeletal imaging, image processing, and computational modeling. In this perspective paper, we outline the motivations for conducting transparent research and provide practical guidelines for implementing it. We start by defining open and reproducible research and describing the benefits and challenges of working transparently. Next, we redefine the outputs of a computational research study as—ideally—a combination of data, code, and a publication, recommend a folder and file structure that reflects these three study outcomes, and describe how to maintain and update such a structure during the study and at study publication. Finally, we emphasize that working in an open and reproducible manner is a learning process, and the best way to acquire the necessary competencies is simply to start.

The Open and Reproducible Musculoskeletal Imaging Research (ORMIR) community is a scientific community dedicated to promoting openness and reproducibility in musculoskeletal imaging, image processing, and computational modelling. In this perspective paper, we outline the motivations for conducting transparent research and provide practical guidelines to implement it. We start with defining open and reproducible research and describing the benefits and challenges of working transparently. Next, we redefine the outputs of a computational research study as—ideally—a combination of data, code, and a publication, recommend a folder and file structure that reflects these three study outcomes, and describe how to maintain and update such a structure during the study and at study publication. Finally, we emphasize that working in an open and reproducible manner is a learning process and the best way to acquire the necessary competencies is simply to start. Lay summary: The ORMIR community promotes openness and reproducibility in musculoskeletal imaging research. In this perspective paper, we explain why transparency matters and recommend how to conduct a computational study in an open and reproducible manner focusing on its three outputs: data, code, and publication. Finally, we highlight that the best way to learn these practices is simply to start.

Abstract Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing. Methods We present MedShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. Results By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Conclusions MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://medshapenet.ikim.nrw/.

—Image registration plays a vital role in understanding changes that occur in 2D and 3D scientific imaging datasets. Registration involves finding a spatial transformation that aligns one image to another by optimizing relevant image similarity metrics. In this paper, we introduce itk-elastix , a user-friendly Python wrapping of the mature elastix registration toolbox. The open-source tool supports rigid, affine, and B-spline deformable registration, making it versatile for various imaging datasets. By utilizing the modular de-sign of itk-elastix , users can efficiently configure and compare different registration methods, and embed these in image analysis workflows.

Shear wave elastography (SWE) is an ultrasound‐based stiffness quantification technology that is used for noninvasive liver fibrosis assessment. However, despite widescale clinical adoption, SWE is largely unused by preclinical researchers and drug developers for studies of liver disease progression in small animal models due to significant experimental, technical, and reproducibility challenges. Therefore, the aim of this work was to develop a tool designed specifically for assessing liver stiffness and echogenicity in small animals to better enable longitudinal preclinical studies. A high‐frequency linear array transducer (12‐24 MHz) was integrated into a robotic small animal ultrasound system (Vega; SonoVol, Inc., Durham, NC) to perform liver stiffness and echogenicity measurements in three dimensions. The instrument was validated with tissue‐mimicking phantoms and a mouse model of nonalcoholic steatohepatitis. Female C57BL/6J mice (n = 40) were placed on choline‐deficient, L‐amino acid‐defined, high‐fat diet and imaged longitudinally for 15 weeks. A subset was sacrificed after each imaging timepoint (n = 5) for histological validation, and analyses of receiver operating characteristic (ROC) curves were performed. Results demonstrated that robotic measurements of echogenicity and stiffness were most strongly correlated with macrovesicular steatosis (R2 = 0.891) and fibrosis (R2 = 0.839), respectively. For diagnostic classification of fibrosis (Ishak score), areas under ROC (AUROCs) curves were 0.969 for ≥Ishak1, 0.984 for ≥Ishak2, 0.980 for ≥Ishak3, and 0.969 for ≥Ishak4. For classification of macrovesicular steatosis (S‐score), AUROCs were 1.00 for ≥S2 and 0.997 for ≥S3. Average scanning and analysis time was <5 minutes/liver. Conclusion: Robotic SWE in small animals is feasible and sensitive to small changes in liver disease state, facilitating in vivo staging of rodent liver disease with minimal sonographic expertise.

We present an open-source web tool for quality control of distributed imaging studies. To minimize the amount of human time and attention spent reviewing the images, we created a neural network to provide an automatic assessment. This steers reviewers’ attention to potentially problematic cases, reducing the likelihood of missing image quality issues. We test our approach using 5-fold cross validation on a set of 5217 magnetic resonance images.

Subject motion during an MRI sequence can cause ghosting effects or diffuse image noise in the phase-encoding direction and hence is likely to bias findings in neuroimaging studies. Detecting motion artifacts often relies on experts visually inspecting MRIs, which is subjective and expensive. To improve this detection, we develop a framework to automatically quantify the severity of motion artifact within a brain MRI. We formulate this task as a regression problem and train the regressor from a data set of MRIs with various amounts of motion artifacts. To resolve the issue of missing fine-grained ground-truth labels (level of artifacts), we propose Adversarial Bayesian Optimization (ABO) to infer the distribution of motion parameters (i.e., rotation and translation) underlying the acquired MRI data and then inject synthetic motion artifacts sampled from that estimated distribution into motion-free MRIs. After training the regressor on the synthetic data, we applied the model to quantify the motion level in 990 MRIs collected by the National Consortium on Alcohol and Neurodevelopment in Adolescence. Results show that the motion level derived by our approach is more reliable than the traditional metric based on Entropy Focus Criterion and manually defined binary labels.