Volume 11 Issue 3 | Journal of Medical Imaging

Journal of Medical Imaging

VOL. 11 · NO. 3 | May 2024

ISSUES IN PROGRESS

IN PROGRESS

SPIE publishes accepted journal articles as soon as they are approved for publication. Journal issues are considered In Progress until all articles for an issue have been published. Articles published ahead of the completed issue are fully citable.

IN THIS ISSUE

Physics of Medical Imaging (2)

Image Processing (3)

Computer-Aided Diagnosis (5)

Image-Guided Procedures, Robotic Interventions, and Modeling (3)

Image Perception, Observer Performance, and Technology Assessment (1)

Biomedical Applications in Molecular, Structural, and Functional Imaging (2)

Ultrasonic Imaging and Tomography (1)

Digital Pathology (2)

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Physics of Medical Imaging

Maximizing microcalcification detectability in low-dose dedicated cone-beam breast CT: parallel cascades-based theoretical analysis

Thomas Larsen, Hsin Wu Tseng, Rachawadee Trinate, Zhiyang Fu, Jing-Tzyh Alan Chiang, Andrew Karellas, Srinivasan Vedantham

Journal of Medical Imaging, Vol. 11, Issue 03, 033501, (May 2024) https://doi.org/10.1117/1.JMI.11.3.033501

TOPICS: X-rays, Scintillators, Tunable filters, Modulation transfer functions, Copper, Breast, Photons, Optical filters, X-ray imaging, Imaging systems

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Can processed images be used to determine the modulation transfer function and detective quantum efficiency?

Lisa Garland, Haechan Yang, Paul Picot, Jesse Tanguay, Ian Cunningham

Journal of Medical Imaging, Vol. 11, Issue 03, 033502, (May 2024) https://doi.org/10.1117/1.JMI.11.3.033502

TOPICS: Modulation transfer functions, Image processing, Sensors, Convolution, Quantum processing, Aliasing, Spatial frequencies, Signal processing, Fourier transforms, Modulation

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Image Processing

Graph neural networks for automatic extraction and labeling of the coronary artery tree in CT angiography

Nils Hampe, Sanne G. M. van Velzen, Jelmer M. Wolterink, Carlos Collet, José P. S. Henriques, Nils Planken, Ivana Išgum

Journal of Medical Imaging, Vol. 11, Issue 03, 034001, (May 2024) https://doi.org/10.1117/1.JMI.11.3.034001

TOPICS: Arteries, Anatomy, Image segmentation, Education and training, Neural networks, Machine learning, Veins, Image resolution, Feature extraction, Automatic tracking

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Fiberscopic pattern removal for optimal coverage in 3D bladder reconstructions of fiberscope cystoscopy videos

Rachel Eimen, Halina Krzyzanowska, Kristen R. Scarpato, Audrey K. Bowden

Journal of Medical Imaging, Vol. 11, Issue 03, 034002, (May 2024) https://doi.org/10.1117/1.JMI.11.3.034002

TOPICS: Video, Cystoscopy, Bladder, 3D modeling, Tunable filters, Reconstruction algorithms, 3D mask effects, Linear filtering, Optical filters, Image filtering

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Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images

Hanya Ahmed, Qianni Zhang, Robert Donnan, Akram Alomainy

Journal of Medical Imaging, Vol. 11, Issue 03, 034008, (April 2024) https://doi.org/10.1117/1.JMI.11.3.034008

TOPICS: Denoising, Optical coherence tomography, Transformers, Speckle, Dentistry, Silver, Visualization, Education and training, Image segmentation, Data modeling

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Computer-Aided Diagnosis

Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal

Lindsay Douglas, Jordan Fuhrman, Qiyuan Hu, Alexandra Edwards, Deepa Sheth, Hiroyuki Abe, Maryellen Giger

Journal of Medical Imaging, Vol. 11, Issue 03, 034501, (May 2024) https://doi.org/10.1117/1.JMI.11.3.034501

TOPICS: Breast, Image segmentation, Education and training, Visualization, Magnetic resonance imaging, Tumors, Statistical analysis, Binary data, Image classification, Breast cancer

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Purpose

Current clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels.

Approach

A U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy c-means clustering was used to segment lesions; the lesion volume was removed prior to creating projections. U-Net outputs were applied to create projection images of both, affected, and unaffected breasts before and after lesion removal. BPE scores were calculated from various projection images, including MIPs or average intensity projections of first- or second postcontrast subtraction MRIs, to evaluate the effect of varying image parameters on automatic BPE assessment. Receiver operating characteristic analysis was performed to determine the predictive value of computed scores in BPE level classification tasks relative to radiologist ratings.

Results

Statistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, p<0.001). Scores from all breast regions performed significantly better than guessing (p<0.025 from the z-test). Results failed to show a statistically significant difference in performance with and without lesion removal. BPE scores of the affected breast in the second postcontrast subtraction MIP after lesion removal performed statistically greater than random guessing across various viewing projections and DCE time points.

Conclusions

Results demonstrate the potential for automatic BPE scoring to serve as a quantitative value for objective BPE level classification from breast DCE-MR without the influence of lesion enhancement.

Quantifying lung fissure integrity using a three-dimensional patch-based convolutional neural network on CT images for emphysema treatment planning

Dallas K. Tada, Pangyu Teng, Kalyani Vyapari, Ashley Banola, George Foster, Esteban Diaz, Grace Hyun J. Kim, Jonathan G. Goldin, Fereidoun Abtin, Michael McNitt-Gray, Matthew S. Brown

Journal of Medical Imaging, Vol. 11, Issue 03, 034502, (May 2024) https://doi.org/10.1117/1.JMI.11.3.034502

TOPICS: Lung, Image segmentation, Voxels, Education and training, Radio over Fiber, Computed tomography, Emphysema, 3D image processing, Visualization, Chronic obstructive pulmonary disease

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Purpose

Evaluation of lung fissure integrity is required to determine whether emphysema patients have complete fissures and are candidates for endobronchial valve (EBV) therapy. We propose a deep learning (DL) approach to segment fissures using a three-dimensional patch-based convolutional neural network (CNN) and quantitatively assess fissure integrity on CT to evaluate it in subjects with severe emphysema.

Approach

From an anonymized image database of patients with severe emphysema, 129 CT scans were used. Lung lobe segmentations were performed to identify lobar regions, and the boundaries among these regions were used to construct approximate interlobar regions of interest (ROIs). The interlobar ROIs were annotated by expert image analysts to identify voxels where the fissure was present and create a reference ROI that excluded non-fissure voxels (where the fissure is incomplete). A CNN configured by nnU-Net was trained using 86 CT scans and their corresponding reference ROIs to segment the ROIs of left oblique fissure (LOF), right oblique fissure (ROF), and right horizontal fissure (RHF). For an independent test set of 43 cases, fissure integrity was quantified by mapping the segmented fissure ROI along the interlobar ROI. A fissure integrity score (FIS) was then calculated as the percentage of labeled fissure voxels divided by total voxels in the interlobar ROI. Predicted FIS (p-FIS) was quantified from the CNN output, and statistical analyses were performed comparing p-FIS and reference FIS (r-FIS).

Results

The absolute percent error mean (±SD) between r-FIS and p-FIS for the test set was 4.0% (±4.1%), 6.0% (±9.3%), and 12.2% (±12.5%) for the LOF, ROF, and RHF, respectively.

Conclusions

A DL approach was developed to segment lung fissures on CT images and accurately quantify FIS. It has potential to assist in the identification of emphysema patients who would benefit from EBV treatment.

Automating aortic cross-sectional measurement of 3D aorta models

Matthew Bramlet, Salman Mohamadi, Jayishnu Srinivas, Tehan Dassanayaka, Tafara Okammor, Mark Shadden, Bradley P. Sutton

Journal of Medical Imaging, Vol. 11, Issue 03, 034503, (May 2024) https://doi.org/10.1117/1.JMI.11.3.034503

TOPICS: 3D modeling, Aorta, 3D metrology, Image segmentation, 3D image processing, 3D mask effects, Arteries, Anatomy, Diagnostics, Image processing

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Purpose

Aortic dissection carries a mortality as high as 50%, but surgical palliation is also fraught with morbidity risks of stroke or paralysis. As such, a significant focus of medical decision making is on longitudinal aortic diameters. We hypothesize that three-dimensional (3D) modeling affords a more efficient methodology toward automated longitudinal aortic measurement. The first step is to automate the measurement of manually segmented 3D models of the aorta. We developed and validated an algorithm to analyze a 3D segmented aorta and output the maximum dimension of minimum cross-sectional areas in a stepwise progression from the diaphragm to the aortic root. Accordingly, the goal is to assess the diagnostic validity of the 3D modeling measurement as a substitute for existing 2D measurements.

Approach

From January 2021 to June 2022, 66 3D non-contrast steady-state free precession magnetic resonance images of aortic pathology with clinical aortic measurements were identified; 3D aorta models were manually segmented. A novel mathematical algorithm was applied to each model to generate maximal aortic diameters from the diaphragm to the root, which were then correlated to clinical measurements.

Results

With a 76% success rate, we analyzed the resulting 50 3D aortic models utilizing the automated measurement tool. There was an excellent correlation between the automated measurement and the clinical measurement. The intra-class correlation coefficient and p-value for each of the nine measured locations of the aorta were as follows: sinus of valsalva, 0.99, <0.001; sino-tubular junction, 0.89, <0.001; ascending aorta, 0.97, <0.001; brachiocephalic artery, 0.96, <0.001; transverse segment 1, 0.89, <0.001; transverse segment 2, 0.93, <0.001; isthmus region, 0.92, <0.001; descending aorta, 0.96, <0.001; and aorta at diaphragm, 0.3, <0.001.

Conclusions

Automating diagnostic measurements that appease clinical confidence is a critical first step in a fully automated process. This tool demonstrates excellent correlation between measurements derived from manually segmented 3D models and the clinical measurements, laying the foundation for transitioning analytic methodologies from 2D to 3D.

Segment anything with inception module for automated segmentation of endometrium in ultrasound images

Yang Qiu, Zhun Xie, Yingchun Jiang, Jianguo Ma

Journal of Medical Imaging, Vol. 11, Issue 03, 034504, (May 2024) https://doi.org/10.1117/1.JMI.11.3.034504

TOPICS: Image segmentation, Ultrasonography, Performance modeling, Data modeling, Education and training, Medical imaging, Diagnostics, Image processing, Machine learning, Deep learning

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Near-pair patch generative adversarial network for data augmentation of focal pathology object detection models

Ethan Tu, Jonathan Burkow, Andy Tsai, Joseph Junewick, Francisco A. Perez, Jeffrey Otjen, Adam M. Alessio

Journal of Medical Imaging, Vol. 11, Issue 03, 034505, (June 2024) https://doi.org/10.1117/1.JMI.11.3.034505

TOPICS: Education and training, Object detection, Radiography, Sensors, Data modeling, Pathology, Gallium nitride, Visualization, Machine learning, Feature extraction

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Purpose

The limited volume of medical training data remains one of the leading challenges for machine learning for diagnostic applications. Object detectors that identify and localize pathologies require training with a large volume of labeled images, which are often expensive and time-consuming to curate. To reduce this challenge, we present a method to support distant supervision of object detectors through generation of synthetic pathology-present labeled images.

Approach

Our method employs the previously proposed cyclic generative adversarial network (cycleGAN) with two key innovations: (1) use of “near-pair” pathology-present regions and pathology-absent regions from similar locations in the same subject for training and (2) the addition of a realism metric (Fréchet inception distance) to the generator loss term. We trained and tested this method with 2800 fracture-present and 2800 fracture-absent image patches from 704 unique pediatric chest radiographs. The trained model was then used to generate synthetic pathology-present images with exact knowledge of location (labels) of the pathology. These synthetic images provided an augmented training set for an object detector.

Results

In an observer study, four pediatric radiologists used a five-point Likert scale indicating the likelihood of a real fracture (1 = definitely not a fracture and 5 = definitely a fracture) to grade a set of real fracture-absent, real fracture-present, and synthetic fracture-present images. The real fracture-absent images scored 1.7±1.0, real fracture-present images 4.1±1.2, and synthetic fracture-present images 2.5±1.2. An object detector model (YOLOv5) trained on a mix of 500 real and 500 synthetic radiographs performed with a recall of 0.57±0.05 and an F2 score of 0.59±0.05. In comparison, when trained on only 500 real radiographs, the recall and F2 score were 0.49±0.06 and 0.53±0.06, respectively.

Conclusions

Our proposed method generates visually realistic pathology and that provided improved object detector performance for the task of rib fracture detection.

Image-Guided Procedures, Robotic Interventions, and Modeling

Self-supervised learning for interventional image analytics: toward robust device trackers

Saahil Islam, Venkatesh N. Murthy, Dominik Neumann, Badhan Kumar Das, Puneet Sharma, Andreas Maier, Dorin Comaniciu, Florin C. Ghesu

Journal of Medical Imaging, Vol. 11, Issue 03, 035001, (May 2024) https://doi.org/10.1117/1.JMI.11.3.035001

TOPICS: Image analysis, Education and training, Analytics, X-rays, Transformers, Performance modeling, Angiography, X-ray imaging, Interpolation, Instrument modeling

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Purpose

The accurate detection and tracking of devices, such as guiding catheters in live X-ray image acquisitions, are essential prerequisites for endovascular cardiac interventions. This information is leveraged for procedural guidance, e.g., directing stent placements. To ensure procedural safety and efficacy, there is a need for high robustness/no failures during tracking. To achieve this, one needs to efficiently tackle challenges, such as device obscuration by the contrast agent or other external devices or wires and changes in the field-of-view or acquisition angle, as well as the continuous movement due to cardiac and respiratory motion.

Approach

To overcome the aforementioned challenges, we propose an approach to learn spatio-temporal features from a very large data cohort of over 16 million interventional X-ray frames using self-supervision for image sequence data. Our approach is based on a masked image modeling technique that leverages frame interpolation-based reconstruction to learn fine inter-frame temporal correspondences. The features encoded in the resulting model are fine-tuned downstream in a light-weight model.

Results

Our approach achieves state-of-the-art performance, in particular for robustness, compared to ultra optimized reference solutions (that use multi-stage feature fusion or multi-task and flow regularization). The experiments show that our method achieves a 66.31% reduction in the maximum tracking error against the reference solutions (23.20% when flow regularization is used), achieving a success score of 97.95% at a 3× faster inference speed of 42 frames-per-second (on GPU). In addition, we achieve a 20% reduction in the standard deviation of errors, which indicates a much more stable tracking performance.

Conclusions

The proposed data-driven approach achieves superior performance, particularly in robustness and speed compared with the frequently used multi-modular approaches for device tracking. The results encourage the use of our approach in various other tasks within interventional image analytics that require effective understanding of spatio-temporal semantics.

Phantom study of augmented reality framework to assist epicardial punctures

Kobe Bamps, Jeroen Bertels, Lennert Minten, Alexis Puvrez, Walter Coudyzer, Stijn De Buck, Joris Ector

Journal of Medical Imaging, Vol. 11, Issue 03, 035002, (May 2024) https://doi.org/10.1117/1.JMI.11.3.035002

TOPICS: Augmented reality, Autoregressive models, Error analysis, Head-mounted displays, Computed tomography, Calibration, Skin, Electrocardiography, Accuracy assessment, 3D modeling

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Adaptive continuation based smooth l0-norm approximation for compressed sensing MR image reconstruction

Sumit Datta, Joseph Paul

Journal of Medical Imaging, Vol. 11, Issue 03, 035003, (May 2024) https://doi.org/10.1117/1.JMI.11.3.035003

TOPICS: Image restoration, Magnetic resonance imaging, Compressed sensing, Reconstruction algorithms, Neuroimaging, Brain, Modulation, Medical image reconstruction, Wavelets, Spine

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Purpose

There are a number of algorithms for smooth l0-norm (SL0) approximation. In most of the cases, sparsity level of the reconstructed signal is controlled by using a decreasing sequence of the modulation parameter values. However, predefined decreasing sequences of the modulation parameter values cannot produce optimal sparsity or best reconstruction performance, because the best choice of the parameter values is often data-dependent and dynamically changes in each iteration.

Approach

We propose an adaptive compressed sensing magnetic resonance image reconstruction using the SL0 approximation method. The SL0 approach typically involves one-step gradient descent of the SL0 approximating function parameterized with a modulation parameter, followed by a projection step onto the feasible solution set. Since the best choice of the parameter values is often data-dependent and dynamically changes in each iteration, it is preferable to adaptively control the rate of decrease of the parameter values. In order to achieve this, we solve two subproblems in an alternating manner. One is a sparse regularization-based subproblem, which is solved with a precomputed value of the parameter, and the second subproblem is the estimation of the parameter itself using a root finding technique.

Results

The advantage of this approach in terms of speed and accuracy is illustrated using a compressed sensing magnetic resonance image reconstruction problem and compared with constant scale factor continuation based SL0-norm and adaptive continuation based l1-norm minimization approaches. The proposed adaptive estimation is found to be at least twofold faster than automated parameter estimation based iterative shrinkage-thresholding algorithm in terms of CPU time, on an average improvement of reconstruction performance 15% in terms of normalized mean squared error.

Conclusions

An adaptive continuation-based SL0 algorithm is presented, with a potential application to compressed sensing (CS)-based MR image reconstruction. It is a data-dependent adaptive continuation method and eliminates the problem of searching for appropriate constant scale factor values to be used in the CS reconstruction of different types of MRI data.

Image Perception, Observer Performance, and Technology Assessment

Perceptual thresholds for differences in CT noise texture

Luuk Oostveen, Kirsten Boedeker, Daniel Shin, Craig Abbey, Ioannis Sechopoulos

Journal of Medical Imaging, Vol. 11, Issue 03, 035501, (May 2024) https://doi.org/10.1117/1.JMI.11.3.035501

TOPICS: Computed tomography, Lung, Affine motion model, Reconstruction algorithms, Medical image reconstruction, Bone, Abdomen, Manufacturing, Covariance matrices, Brain

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Biomedical Applications in Molecular, Structural, and Functional Imaging

Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis

Christian Herz, Nicolas Vergnet, Sijie Tian, Abdullah Aly, Matthew Jolley, Nathanael Tran, Gabriel Arenas, Andras Lasso, Nadav Schwartz, Kathleen O’Neill, Paul Yushkevich, Alison Pouch

Journal of Medical Imaging, Vol. 11, Issue 03, 036001, (May 2024) https://doi.org/10.1117/1.JMI.11.3.036001

TOPICS: Deformation, Anatomy, Modeling, Image segmentation, 3D modeling, Human-machine interfaces, Design, Medical imaging, Biomedical applications, Shape analysis

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Purpose

Deformable medial modeling is an inverse skeletonization approach to representing anatomy in medical images, which can be used for statistical shape analysis and assessment of patient-specific anatomical features such as locally varying thickness. It involves deforming a pre-defined synthetic skeleton, or template, to anatomical structures of the same class. The lack of software for creating such skeletons has been a limitation to more widespread use of deformable medial modeling. Therefore, the objective of this work is to present an open-source user interface (UI) for the creation of synthetic skeletons for a range of medial modeling applications in medical imaging.

Approach

A UI for interactive design of synthetic skeletons was implemented in 3D Slicer, an open-source medical image analysis application. The steps in synthetic skeleton design include importation and skeletonization of a 3D segmentation, followed by interactive 3D point placement and triangulation of the medial surface such that the desired branching configuration of the anatomical structure’s medial axis is achieved. Synthetic skeleton design was evaluated in five clinical applications. Compatibility of the synthetic skeletons with open-source software for deformable medial modeling was tested, and representational accuracy of the deformed medial models was evaluated.

Results

Three users designed synthetic skeletons of anatomies with various topologies: the placenta, aortic root wall, mitral valve, cardiac ventricles, and the uterus. The skeletons were compatible with skeleton-first and boundary-first software for deformable medial modeling. The fitted medial models achieved good representational accuracy with respect to the 3D segmentations from which the synthetic skeletons were generated.

Conclusions

Synthetic skeleton design has been a practical challenge in leveraging deformable medial modeling for new clinical applications. This work demonstrates an open-source UI for user-friendly design of synthetic skeletons for anatomies with a wide range of topologies.

Automatic lesion detection for narrow-band imaging bronchoscopy

Vahid Daneshpajooh, Danish Ahmad, Jennifer Toth, Rebecca Bascom, William Higgins

Journal of Medical Imaging, Vol. 11, Issue 03, 036002, (May 2024) https://doi.org/10.1117/1.JMI.11.3.036002

TOPICS: Video, Object detection, Education and training, Bronchoscopy, Cancer detection, Endoscopy, Lung cancer, Video processing, Visualization, Databases

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Purpose

Early detection of cancer is crucial for lung cancer patients, as it determines disease prognosis. Lung cancer typically starts as bronchial lesions along the airway walls. Recent research has indicated that narrow-band imaging (NBI) bronchoscopy enables more effective bronchial lesion detection than other bronchoscopic modalities. Unfortunately, NBI video can be hard to interpret because physicians currently are forced to perform a time-consuming subjective visual search to detect bronchial lesions in a long airway-exam video. As a result, NBI bronchoscopy is not regularly used in practice. To alleviate this problem, we propose an automatic two-stage real-time method for bronchial lesion detection in NBI video and perform a first-of-its-kind pilot study of the method using NBI airway exam video collected at our institution.

Approach

Given a patient’s NBI video, the first method stage entails a deep-learning-based object detection network coupled with a multiframe abnormality measure to locate candidate lesions on each video frame. The second method stage then draws upon a Siamese network and a Kalman filter to track candidate lesions over multiple frames to arrive at final lesion decisions.

Results

Tests drawing on 23 patient NBI airway exam videos indicate that the method can process an incoming video stream at a real-time frame rate, thereby making the method viable for real-time inspection during a live bronchoscopic airway exam. Furthermore, our studies showed a 93% sensitivity and 86% specificity for lesion detection; this compares favorably to a sensitivity and specificity of 80% and 84% achieved over a series of recent pooled clinical studies using the current time-consuming subjective clinical approach.

Conclusion

The method shows potential for robust lesion detection in NBI video at a real-time frame rate. Therefore, it could help enable more common use of NBI bronchoscopy for bronchial lesion detection.

Ultrasonic Imaging and Tomography

Validation of ultrasound velocimetry and computational fluid dynamics for flow assessment in femoral artery stenotic disease

Lennart van de Velde, Majorie van Helvert, Stefan Engelhard, Ashkan Ghanbarzadeh-Dagheyan, Hadi Mirgolbabaee, Jason Voorneveld, Guillaume Lajoinie, Michel Versluis, Michel M. P. J. Reijnen, Erik Groot Jebbink

Journal of Medical Imaging, Vol. 11, Issue 03, 037001, (May 2024) https://doi.org/10.1117/1.JMI.11.3.037001

TOPICS: Ultrasonography, Velocimetry, Arteries, Computational fluid dynamics, Boundary conditions, Hemodynamics, Video, Turbulence, Particle image velocimetry, Doppler effect

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Purpose

To investigate the accuracy of high-framerate echo particle image velocimetry (ePIV) and computational fluid dynamics (CFD) for determining velocity vectors in femoral bifurcation models through comparison with optical particle image velocimetry (oPIV).

Approach

Separate femoral bifurcation models were built for oPIV and ePIV measurements of a non-stenosed (control) and a 75%-area stenosed common femoral artery. A flow loop was used to create triphasic pulsatile flow. In-plane velocity vectors were measured with oPIV and ePIV. Flow was simulated with CFD using boundary conditions from ePIV and additional duplex-ultrasound (DUS) measurements. Mean differences and 95%-limits of agreement (1.96*SD) of the velocity magnitudes in space and time were compared, and the similarity of vector complexity (VC) and time-averaged wall shear stress (TAWSS) was assessed.

Results

Similar flow features were observed between modalities with velocities up to 110 and 330 cm/s in the control and the stenosed model, respectively. Relative to oPIV, ePIV and CFD-ePIV showed negligible mean differences in velocity (<3 cm/s), with limits of agreement of ±25 cm/s (control) and ±34 cm/s (stenosed). CFD-DUS overestimated velocities with limits of agreements of 13±40 and 16.1±55 cm/s for the control and stenosed model, respectively. VC showed good agreement, whereas TAWSS showed similar trends but with higher values for ePIV, CFD-DUS, and CFD-ePIV compared to oPIV.

Conclusions

EPIV and CFD-ePIV can accurately measure complex flow features in the femoral bifurcation and around a stenosis. CFD-DUS showed larger deviations in velocities making it a less robust technique for hemodynamical assessment. The applied ePIV and CFD techniques enable two- and three-dimensional assessment of local hemodynamics with high spatiotemporal resolution and thereby overcome key limitations of current clinical modalities making them an attractive and cost-effective alternative for hemodynamical assessment in clinical practice.

Digital Pathology

Multiresolution semantic segmentation of biological structures in digital histopathology

Sina Salsabili, Adrian D. Chan, Eranga Ukwatta

Journal of Medical Imaging, Vol. 11, Issue 03, 037501, (May 2024) https://doi.org/10.1117/1.JMI.11.3.037501

TOPICS: Image segmentation, Semantics, Histopathology, Education and training, Lung, Blood vessels, Spatial resolution, Image resolution, Tissues, Performance modeling

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WhARIO: whole-slide-image-based survival analysis for patients treated with immunotherapy

Paul Tourniaire, Marius Ilie, Julien Mazières, Anna Vigier, François Ghiringhelli, Nicolas Piton, Jean-Christophe Sabourin, Frédéric Bibeau, Paul Hofman, Nicholas Ayache, Hervé Delingette

Journal of Medical Imaging, Vol. 11, Issue 03, 037502, (May 2024) https://doi.org/10.1117/1.JMI.11.3.037502

TOPICS: Tissues, Tumors, Education and training, Matrices, Lung cancer, Lawrencium, Feature extraction, Biological research, Cross validation, Pathology

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Purpose

Immune checkpoint inhibitors (ICIs) are now one of the standards of care for patients with lung cancer and have greatly improved both progression-free and overall survival, although <20% of the patients respond to the treatment, and some face acute adverse events. Although a few predictive biomarkers have integrated the clinical workflow, they require additional modalities on top of whole-slide images and lack efficiency or robustness. In this work, we propose a biomarker of immunotherapy outcome derived solely from the analysis of histology slides.

Approach

We develop a three-step framework, combining contrastive learning and nonparametric clustering to distinguish tissue patterns within the slides, before exploiting the adjacencies of previously defined regions to derive features and train a proportional hazards model for survival analysis. We test our approach on an in-house dataset of 193 patients from 5 medical centers and compare it with the gold standard tumor proportion score (TPS) biomarker.

Results

On a fivefold cross-validation (CV) of the entire dataset, the whole-slide image-based survival analysis for patients treated with immunotherapy (WhARIO) features are able to separate a low- and a high-risk group of patients with a hazard ratio (HR) of 2.29 (CI95=1.48 to 3.56), whereas the TPS 1% reference threshold only reaches a HR of 1.81 (CI95=1.21 to 2.69). Combining the two yields a higher HR of 2.60 (CI95=1.72 to 3.94). Additional experiments on the same dataset, where one out of five centers is excluded from the CV and used as a test set, confirm these trends.

Conclusions

Our uniquely designed WhARIO features are an efficient predictor of survival for lung cancer patients who received ICI treatment. We achieve similar performance to the current gold standard biomarker, without the need to access other imaging modalities, and show that both can be used together to reach even better results.

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