Volume 11 Issue 4 | Journal of Medical Imaging

Journal of Medical Imaging

VOL. 11 · NO. 4 | July 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

Image Processing (3)

Computer-Aided Diagnosis (3)

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

Biomedical Applications in Molecular, Structural, and Functional Imaging (1)

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Image Processing

Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation

Simone Bendazzoli, Emelie Bäcklin, Örjan Smedby, Birgitta Janerot-Sjoberg, Bryan Connolly, Chunliang Wang

Journal of Medical Imaging, Vol. 11, Issue 04, 044001, (July 2024) https://doi.org/10.1117/1.JMI.11.4.044001

TOPICS: Lung, Image segmentation, Anatomy, Education and training, COVID 19, Prior knowledge, Voxels, Performance modeling, Computed tomography, Network architectures

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Projected pooling loss for red nucleus segmentation with soft topology constraints

Guanghui Fu, Rosana El Jurdi, Lydia Chougar, Didier Dormont, Romain Valabregue, Stéphane Lehéricy, Olivier Colliot, The ICEBERG Study Group

Journal of Medical Imaging, Vol. 11, Issue 04, 044002, (July 2024) https://doi.org/10.1117/1.JMI.11.4.044002

TOPICS: Image segmentation, Education and training, Anatomy, Voxels, Optical spheres, Spleen, Prior knowledge, Deep learning, Medical imaging, Heart

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Optimizing neurointerventional procedures: an algorithm for embolization coil detection and automated collimation to enable dose reduction

Arpitha Ravi, Philipp Bernhardt, Mathis Hoffmann, Richard Obler, Cuong Nguyen, Andreas Berting, René Chapot, Andreas Maier

Journal of Medical Imaging, Vol. 11, Issue 04, 044003, (July 2024) https://doi.org/10.1117/1.JMI.11.4.044003

TOPICS: Collimation, Data modeling, Object detection, Blob detection, Sensors, Education and training, Deep learning, Mathematical optimization, X-rays, Image processing

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Purpose

Monitoring radiation dose and time parameters during radiological interventions is crucial, especially in neurointerventional procedures, such as aneurysm treatment with embolization coils. The algorithm presented detects the presence of these embolization coils in medical images. It establishes a bounding box as a reference for automated collimation, with the primary objective being to enhance the efficiency and safety of neurointerventional procedures by actively optimizing image quality while minimizing patient dose.

Methods

Two distinct methodologies are evaluated in our study. The first involves deep learning, employing the Faster R-CNN model with a ResNet-50 FPN as a backbone and a RetinaNet model. The second method utilizes a classical blob detection approach, serving as a benchmark for comparison.

Results

We performed a fivefold cross-validation, and our top-performing model achieved mean mAP@75 of 0.84 across all folds on validation data and mean mAP@75 of 0.94 on independent test data. Since we use an upscaled bounding box, achieving 100% overlap between ground truth and prediction is not necessary. To highlight the real-world applications of our algorithm, we conducted a simulation featuring a coil constructed from an alloy wire, effectively showcasing the implementation of automatic collimation. This resulted in a notable reduction in the dose area product, signifying the reduction of stochastic risks for both patients and medical staff by minimizing scatter radiation. Additionally, our algorithm assists in avoiding extreme brightness or darkness in X-ray angiography images during narrow collimation, ultimately streamlining the collimation process for physicians.

Conclusion

To our knowledge, this marks the initial attempt at an approach successfully detecting embolization coils, showcasing the extended applications of integrating detection results into the X-ray angiography system. The method we present has the potential for broader application, allowing its extension to detect other medical objects utilized in interventional procedures.

Computer-Aided Diagnosis

Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected in vivo lesions

Daniel Liang, David Liang, Marc J. Pomeroy, Yongfeng Gao, Licheng Kuo, Lihong Li

Journal of Medical Imaging, Vol. 11, Issue 04, 044501, (July 2024) https://doi.org/10.1117/1.JMI.11.4.044501

TOPICS: Feature extraction, Image classification, Computed tomography, Tissues, Polyps, Machine learning, In vivo imaging, X-ray computed tomography, Medical imaging, Computer aided detection

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Purpose

Medical imaging-based machine learning (ML) for computer-aided diagnosis of in vivo lesions consists of two basic components or modules of (i) feature extraction from non-invasively acquired medical images and (ii) feature classification for prediction of malignancy of lesions detected or localized in the medical images. This study investigates their individual performances for diagnosis of low-dose computed tomography (CT) screening-detected lesions of pulmonary nodules and colorectal polyps.

Approach

Three feature extraction methods were investigated. One uses the mathematical descriptor of gray-level co-occurrence image texture measure to extract the Haralick image texture features (HFs). One uses the convolutional neural network (CNN) architecture to extract deep learning (DL) image abstractive features (DFs). The third one uses the interactions between lesion tissues and X-ray energy of CT to extract tissue-energy specific characteristic features (TFs). All the above three categories of extracted features were classified by the random forest (RF) classifier with comparison to the DL-CNN method, which reads the images, extracts the DFs, and classifies the DFs in an end-to-end manner. The ML diagnosis of lesions or prediction of lesion malignancy was measured by the area under the receiver operating characteristic curve (AUC). Three lesion image datasets were used. The lesions’ tissue pathological reports were used as the learning labels.

Results

Experiments on the three datasets produced AUC values of 0.724 to 0.878 for the HFs, 0.652 to 0.965 for the DFs, and 0.985 to 0.996 for the TFs, compared to the DL-CNN of 0.694 to 0.964. These experimental outcomes indicate that the RF classifier performed comparably to the DL-CNN classification module and the extraction of tissue-energy specific characteristic features dramatically improved AUC value.

Conclusions

The feature extraction module is more important than the feature classification module. Extraction of tissue-energy specific characteristic features is more important than extraction of image abstractive and characteristic features.

Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach

Jenita Manokaran, Richa Mittal, Eranga Ukwatta

Journal of Medical Imaging, Vol. 11, Issue 04, 044502, (July 2024) https://doi.org/10.1117/1.JMI.11.4.044502

TOPICS: Cancer detection, Object detection, Education and training, Lung cancer, Tumor growth modeling, Lung, Data modeling, Computed tomography, Performance modeling, Deep learning

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Purpose

Lung cancer is the second most common cancer and the leading cause of cancer death globally. Low dose computed tomography (LDCT) is the recommended imaging screening tool for the early detection of lung cancer. A fully automated computer-aided detection method for LDCT will greatly improve the existing clinical workflow. Most of the existing methods for lung detection are designed for high-dose CTs (HDCTs), and those methods cannot be directly applied to LDCTs due to domain shifts and inferior quality of LDCT images. In this work, we describe a semi-automated transfer learning-based approach for the early detection of lung nodules using LDCTs.

Approach

In this work, we developed an algorithm based on the object detection model, you only look once (YOLO) to detect lung nodules. The YOLO model was first trained on CTs, and the pre-trained weights were used as initial weights during the retraining of the model on LDCTs using a medical-to-medical transfer learning approach. The dataset for this study was from a screening trial consisting of LDCTs acquired from 50 biopsy-confirmed lung cancer patients obtained over 3 consecutive years (T1, T2, and T3). About 60 lung cancer patients’ HDCTs were obtained from a public dataset. The developed model was evaluated using a hold-out test set comprising 15 patient cases (93 slices with cancerous nodules) using precision, specificity, recall, and F1-score. The evaluation metrics were reported patient-wise on a per-year basis and averaged for 3 years. For comparative analysis, the proposed detection model was trained using pre-trained weights from the COCO dataset as the initial weights. A paired t-test and chi-squared test with an alpha value of 0.05 were used for statistical significance testing.

Results

The results were reported by comparing the proposed model developed using HDCT pre-trained weights with COCO pre-trained weights. The former approach versus the latter approach obtained a precision of 0.982 versus 0.93 in detecting cancerous nodules, specificity of 0.923 versus 0.849 in identifying slices with no cancerous nodules, recall of 0.87 versus 0.886, and F1-score of 0.924 versus 0.903. As the nodule progressed, the former approach achieved a precision of 1, specificity of 0.92, and sensitivity of 0.930. The statistical analysis performed in the comparative study resulted in a p-value of 0.0054 for precision and a p-value of 0.00034 for specificity.

Conclusions

In this study, a semi-automated method was developed to detect lung nodules in LDCTs using HDCT pre-trained weights as the initial weights and retraining the model. Further, the results were compared by replacing HDCT pre-trained weights in the above approach with COCO pre-trained weights. The proposed method may identify early lung nodules during the screening program, reduce overdiagnosis and follow-ups due to misdiagnosis in LDCTs, start treatment options in the affected patients, and lower the mortality rate.

Learning carotid vessel wall segmentation in black-blood MRI using sparsely sampled cross-sections from 3D data

Hinrich Rahlfs, Markus Hüllebrand, Sebastian Schmitter, Christoph Strecker, Andreas Harloff, Anja Hennemuth

Journal of Medical Imaging, Vol. 11, Issue 04, 044503, (July 2024) https://doi.org/10.1117/1.JMI.11.4.044503

TOPICS: Image segmentation, Arteries, Education and training, Magnetic resonance imaging, Data modeling, Independent component analysis, Simulation of CCA and DLA aggregates, 3D image processing, Scanners, Performance modeling

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Image Perception, Observer Performance, and Technology Assessment

Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images

Devi Klein, Srijita Karmakar, Aditya Jonnalagadda, Craig Abbey, Miguel Eckstein

Journal of Medical Imaging, Vol. 11, Issue 04, 045501, (July 2024) https://doi.org/10.1117/1.JMI.11.4.045501

TOPICS: Signal detection, Computer aided detection, Digital breast tomosynthesis, 3D modeling, 3D image processing, Eye, Visualization, Education and training, Deep learning, Picture Archiving and Communication System

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Biomedical Applications in Molecular, Structural, and Functional Imaging

Machine learning and magnetic resonance image texture analysis predicts accelerated lung function decline in ex-smokers with and without chronic obstructive pulmonary disease

Maksym Sharma, Miranda Kirby, Aaron Fenster, David G. McCormack, Grace Parraga

Journal of Medical Imaging, Vol. 11, Issue 04, 046001, (July 2024) https://doi.org/10.1117/1.JMI.11.4.046001

TOPICS: Magnetic resonance imaging, Lung, Machine learning, Image analysis, Education and training, Optical spheres, Chronic obstructive pulmonary disease, Magnetism, Cooccurrence matrices, Voxels

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Purpose

Our objective was to train machine-learning algorithms on hyperpolarized He3 magnetic resonance imaging (MRI) datasets to generate models of accelerated lung function decline in participants with and without chronic-obstructive-pulmonary-disease. We hypothesized that hyperpolarized gas MRI ventilation, machine-learning, and multivariate modeling could be combined to predict clinically-relevant changes in forced expiratory volume in 1 s (FEV1) across 3 years.

Approach

Hyperpolarized He3 MRI was acquired using a coronal Cartesian fast gradient recalled echo sequence with a partial echo and segmented using a k-means clustering algorithm. A maximum entropy mask was used to generate a region-of-interest for texture feature extraction using a custom-developed algorithm and the PyRadiomics platform. The principal component and Boruta analyses were used for feature selection. Ensemble-based and single machine-learning classifiers were evaluated using area-under-the-receiver-operator-curve and sensitivity-specificity analysis.

Results

We evaluated 88 ex-smoker participants with 31±7 months follow-up data, 57 of whom (22 females/35 males, 70±9 years) had negligible changes in FEV1 and 31 participants (7 females/24 males, 68±9 years) with worsening FEV1≥60 mL/year. In addition, 3/88 ex-smokers reported a change in smoking status. We generated machine-learning models to predict FEV1 decline using demographics, spirometry, and texture features, with the later yielding the highest classification accuracy of 81%. The combined model (trained on all available measurements) achieved the overall best classification accuracy of 82%; however, it was not significantly different from the model trained on MRI texture features alone.

Conclusion

For the first time, we have employed hyperpolarized He3 MRI ventilation texture features and machine-learning to identify ex-smokers with accelerated decline in FEV1 with 82% accuracy.

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