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 (2)

Computer-Aided Diagnosis (2)

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

< Previous Issue | Next Issue >

Receive Email Alerts

VIEW ALL ABSTRACTS +

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

Read Abstract +

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

Read Abstract +

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

Read Abstract +

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

Read Abstract +

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.

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 S. Klein, Srijita Karmakar, Aditya Jonnalagadda, Craig K. Abbey, Miguel P. 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

Read Abstract +

Purpose

Approach

Results

Conclusions

Purpose

Approach

Results

Conclusions

Purpose

Approach

Results

Conclusions

Purpose

Approach

Results

Conclusions

Purpose

Approach

Results

Conclusion

Keywords/Phrases

Search In:

Publication Years