Segmentation of Skeleton and Organs in Whole-Body CT Images via Iterative Trilateration

On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies

Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.

Intelligent Labeling of Tumor Lesions Based on Positron Emission Tomography/Computed Tomography

Positron emission tomography/computed tomography (PET/CT) plays a vital role in diagnosing tumors. However, PET/CT imaging relies primarily on manual interpretation and labeling by medical professionals. An enormous workload will affect the training samples' construction for deep learning. The labeling of tumor lesions in PET/CT images involves the intersection of computer graphics and medicine, such as registration, a fusion of medical images, and labeling of lesions. This paper extends the linear interpolation, enhances it in a specific area of the PET image, and uses the outer frame scaling of the PET/CT image and the least-squares residual affine method. The PET and CT images are subjected to wavelet transformation and then synthesized in proportion to form a PET/CT fusion image. According to the absorption of 18F-FDG (fluoro deoxy glucose) SUV in the PET image, the professionals randomly select a point in the focus area in the fusion image, and the system will automatically select the seed point of the focus area to delineate the tumor focus with the regional growth method. Finally, the focus delineated on the PET and CT fusion images is automatically mapped to CT images in the form of polygons, and rectangular segmentation and labeling are formed. This study took the actual PET/CT of patients with lymphatic cancer as an example. The semiautomatic labeling of the system and the manual labeling of imaging specialists were compared and verified. The recognition rate was 93.35%, and the misjudgment rate was 6.52%.

Role of Artificial Intelligence in Theranostics:: Toward Routine Personalized Radiopharmaceutical Therapies

We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of radiopharmaceutical therapies (RPTs). Personalized RPTs require patient-specific dosimetry calculations accompanying therapy. Additionally we discuss the potential to exploit biological information from diagnostic and therapeutic molecular images to derive biomarkers for absorbed dose and outcome prediction; toward personalization of therapies. We try to motivate the nuclear medicine community to expand and align efforts into making routine and reliable personalization of RPTs a reality.

Semiautomatically Quantified Tumor Volume Using 68Ga-PSMA-11 PET as a Biomarker for Survival in Patients with Advanced Prostate Cancer

Prostate-specific membrane antigen (PSMA)-targeting PET imaging is becoming the reference standard for prostate cancer staging, especially in advanced disease. Yet, the implications of PSMA PET-derived whole-body tumor volume for overall survival are poorly elucidated to date. This might be because semiautomated quantification of whole-body tumor volume as a PSMA PET biomarker is an unmet clinical challenge. Therefore, in the present study we propose and evaluate a software that enables the semiautomated quantification of PSMA PET biomarkers such as whole-body tumor volume. Methods: The proposed quantification is implemented as a research prototype. PSMA-accumulating foci were automatically segmented by a percental threshold (50% of local SUV_max). Neural networks were trained to segment organs in PET/CT acquisitions (training CTs: 8,632, validation CTs: 53). Thereby, PSMA foci within organs of physiologic PSMA uptake were semiautomatically excluded from the analysis. Pretherapeutic PSMA PET/CTs of 40 consecutive patients treated with ¹⁷⁷Lu-PSMA-617 were evaluated in this analysis. The whole-body tumor volume (PSMA_TV50), SUV_max, SUV_mean, and other whole-body imaging biomarkers were calculated for each patient. Semiautomatically derived results were compared with manual readings in a subcohort (by 1 nuclear medicine physician). Additionally, an interobserver evaluation of the semiautomated approach was performed in a subcohort (by 2 nuclear medicine physicians). Results: Manually and semiautomatically derived PSMA metrics were highly correlated (PSMA_TV50: R ² = 1.000, P < 0.001; SUV_max: R ² = 0.988, P < 0.001). The interobserver agreement of the semiautomated workflow was also high (PSMA_TV50: R ² = 1.000, P < 0.001, interclass correlation coefficient = 1.000; SUV_max: R ² = 0.988, P < 0.001, interclass correlation coefficient = 0.997). PSMA_TV50 (ml) was a significant predictor of overall survival (hazard ratio: 1.004; 95% confidence interval: 1.001-1.006, P = 0.002) and remained so in a multivariate regression including other biomarkers (hazard ratio: 1.004; 95% confidence interval: 1.001-1.006 P = 0.004). Conclusion: PSMA_TV50 is a promising PSMA PET biomarker that is reproducible and easily quantified by the proposed semiautomated software. Moreover, PSMA_TV50 is a significant predictor of overall survival in patients with advanced prostate cancer who receive ¹⁷⁷Lu-PSMA-617 therapy.

[Semantic Segmentation of Eight Regions of Upper and Lower Limb Bones Using 3D U-Net in Whole-body CT Images]

PURPOSE

Automated analysis of skeletal muscle in whole-body computed tomography (CT) images uses bone information, but bone segmentation including the epiphysis is not achieved. The purpose of this research was the semantic segmentation of eight regions of upper and lower limb bones including the epiphysis in whole-body CT images. Our targets were left and right upper arms, forearms, thighs, and lower legs.

METHOD

We connected two 3D U-Nets in cascade for segmentation of eight upper and lower limb bones in whole-body CT images. The first 3D U-Net was used for skeleton segmentation in whole-body CT images, and the second 3D U-Net was used for eight upper and lower limb bones' segmentation in skeleton segmentation results. Thirty cases of whole-body CT images were used in the experiment, and the segmentation results were evaluated using Dice coefficient with 3-fold cross-validation.

RESULT

The mean Dice coefficient was 93% in the left and right upper arms, 89% in the left and right forearms, 95% in the left and right thighs, and 94% in the left and right lower legs.

CONCLUSION

Although the accuracy of the segmentation results of relatively small bones remains a challenge, the semantic segmentation of eight regions of upper and lower limb bones including the epiphysis in whole-body CT images has been achieved.

qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using 68Ga-PSMA11 PET/CT

Our aim was to introduce and validate qPSMA, a semiautomatic software package for whole-body tumor burden assessment in prostate cancer patients using ⁶⁸Ga-prostate-specific membrane antigen (PSMA) 11 PET/CT. Methods: qPSMA reads hybrid PET/CT images in DICOM format. Its pipeline was written using Python and C++ languages. A bone mask based on CT and a normal-uptake mask including organs with physiologic ⁶⁸Ga-PSMA11 uptake are automatically computed. An SUV threshold of 3 and a liver-based threshold are used to segment bone and soft-tissue lesions, respectively. Manual corrections can be applied using different tools. Multiple output parameters are computed, that is, PSMA ligand-positive tumor volume (PSMA-TV), PSMA ligand-positive total lesion (PSMA-TL), PSMA SUV_mean, and PSMA SUV_max Twenty ⁶⁸Ga-PSMA11 PET/CT data sets were used to validate and evaluate the performance characteristics of qPSMA. Four analyses were performed: validation of the semiautomatic algorithm for liver background activity determination, assessment of intra- and interobserver variability, validation of data from qPSMA by comparison with Syngo.via, and assessment of computational time and comparison of PSMA PET-derived parameters with serum prostate-specific antigen. Results: Automatic liver background calculation resulted in a mean relative difference of 0.74% (intraclass correlation coefficient [ICC], 0.996; 95%CI, 0.989;0.998) compared with METAVOL. Intra- and interobserver variability analyses showed high agreement (all ICCs > 0.990). Quantitative output parameters were compared for 68 lesions. Paired t testing showed no significant differences between the values obtained with the 2 software packages. The ICC estimates obtained for PSMA-TV, PSMA-TL, SUV_mean, and SUV_max were 1.000 (95%CI, 1.000;1.000), 1.000 (95%CI, 1.000;1.000), 0.995 (95%CI, 0.992;0.997), and 0.999 (95%CI, 0.999;1.000), respectively. The first and second reads for intraobserver variability resulted in mean computational times of 13.63 min (range, 8.22-25.45 min) and 9.27 min (range, 8.10-12.15 min), respectively (P = 0.001). Highly significant correlations were found between serum prostate-specific antigen value and both PSMA-TV (r = 0.72, P < 0.001) and PSMA-TL (r = 0.66, P = 0.002). Conclusion: Semiautomatic analyses of whole-body tumor burden in ⁶⁸Ga-PSMA11 PET/CT is feasible. qPSMA is a robust software package that can help physicians quantify tumor load in heavily metastasized prostate cancer patients.