An Efficient Pipeline for Abdomen Segmentation in CT Images

Lymph Node Mapping for Tumor Micrometastasis

Lymph node mapping for tumor micrometastasis is of great significance for the prevention, prognosis, and treatment of cancer. Currently, the traditional clinical detection methods (computed tomography, magnetic resonance imaging, or positron emission tomography/computed tomography) in clinical lymph node mapping still have some inherent disadvantages, which have prompted the development of various fluorescent probes for lymph node mapping. However, the conventional fluorescent probes such as indocyanine green or methylene blue in lymph node mapping are still accompanied by several problems such as impaired surgical field vision due to dye staining or less accumulation and shorter retention time in the lymph node. In a recent achievement, newly designed nanoparticles are prepared with novel properties that could be attractive for lymph node mapping. In this review, we will provide details on the progress of various nanoparticles for lymph node mapping and emphasize other multivariant properties in different nanoparticles, including strong tumor-targeting affinity and specificity, self-luminescence, and even with the function to kill metastatic cancer cells.

Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans

RATIONALE AND OBJECTIVES

To develop and evaluate an automated machine learning (ML) algorithm for segmenting the paraspinous muscles on chest computed tomography (CT) scans to evaluate for presence of sarcopenia.

MATERIALS AND METHODS

A convolutional neural network based on the U-Net architecture was trained to perform muscle segmentation on a dataset of 1875 single slice CT images and was tested on 209 CT images of participants in the National Lung Screening Trial. Low-dose, noncontrast CT examinations were obtained at 33 clinical sites, using scanners from four manufacturers. The study participants had a mean age of 71.6 years (range, 70-74 years). Ground truth was obtained by manually segmenting the left paraspinous muscle at the level of the T12 vertebra. Muscle cross-sectional area (CSA) and muscle attenuation (MA) were recorded. Comparison between the ML algorithm and ground truth measures of muscle CSA and MA were obtained using Dice similarity coefficients and Pearson correlations.

RESULTS

Compared to ground truth segmentation, the ML algorithm achieved median (standard deviation) Dice scores of 0.94 (0.04) in the test set. Mean (SD) muscle CSA was 14.3 (3.6) cm2 for ground truth and 13.7 (3.5) cm2 for ML segmentation. Mean (SD) MA was 41.6 (7.6) Hounsfield units (HU) for ground truth and 43.5 (7.9) HU for ML segmentation. There was high correlation between ML algorithm and ground truth for muscle CSA (r2 = 0.86; p < 0.0001) and MA (r2 = 0.95; p < 0.0001).

CONCLUSION

The ML algorithm for measurement of paraspinous muscles compared favorably to manual ground truth measurements in the NLST. The algorithm generalized well to a heterogeneous set of low-dose CT images and may be capable of automated quantification of muscle metrics to screen for sarcopenia on routine chest CT examinations.