Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT

Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood polynomial fitting approach

PURPOSE

The purpose of this work was twofold: (a) To provide a robust and accurate method for image segmentation of dedicated breast CT (bCT) volume data sets, and (b) to improve Hounsfield unit (HU) accuracy in bCT by means of a postprocessing method that uses the segmented images to correct for the low-frequency shading artifacts in reconstructed images.

METHODS

A sequential and iterative application of image segmentation and low-order polynomial fitting to bCT volume data sets was used in the interleaved correction (IC) method. Image segmentation was performed through a deep convolutional neural network (CNN) with a modified U-Net architecture. A total of 45 621 coronal bCT images from 111 patient volume data sets were segmented (using a previously published segmentation algorithm) and used for neural network training, validation, and testing. All patient data sets were selected from scans performed on four different prototype breast CT systems. The adipose voxels for each patient volume data set, segmented using the proposed CNN, were then fit to a three-dimensional low-order polynomial. The polynomial fit was subsequently used to correct for the shading artifacts introduced by scatter and beam hardening in a method termed "flat fielding." An interleaved utilization of image segmentation and flat fielding was repeated until a convergence criterion was satisfied. Mathematical and physical phantom studies were conducted to evaluate the dependence of the proposed algorithm on breast size and the distribution of fibroglandular tissue. In addition, a subset of patient scans (not used in the CNN training, testing or validation) were used to investigate the accuracy of the IC method across different scanner designs and beam qualities.

RESULTS

The IC method resulted in an accurate classification of different tissue types with an average Dice similarity coefficient > 95%, precision > 97%, recall > 95%, and F1-score > 96% across all tissue types. The flat fielding correction of bCT images resulted in a significant reduction in either cupping or capping artifacts in both mathematical and physical phantom studies as measured by the integral nonuniformity metric with an average reduction of 71% for cupping and 30% for capping across different phantom sizes, and the Uniformity Index with an average reduction of 53% for cupping and 34% for capping.

CONCLUSION

The validation studies demonstrated that the IC method improves Hounsfield Units (HU) accuracy and effectively corrects for shading artifacts caused by scatter contamination and beam hardening. The postprocessing approach described herein is relevant to the broad scope of bCT devices and does not require any modification in hardware or existing scan protocols. The trained CNN parameters and network architecture are available for interested users.

Postoperative recurrence analysis of breast cancer patients based on clinical serum markers using discriminant methods

BACKGROUND

Breast cancer is a common gynecological malignant tumor and currently its clinical diagnosis mainly depends on methods of iconography and measurement of serum level.

OBJECTIVE

To analyze correlation between serum index levels and prognosis of patients with breast cancer in one week and six months after operation, and to establish support vector machine (SVM) model to evaluate its effectiveness.

METHODS

One hundred sixty eight patients diagnosed with breast cancer at Affiliated Cancer Hospital of Zhengzhou University were collected, 46 of which did palindromia while other 122 didn't six months after operation. Serum CA153, CA125 and CEA levels of different periods in two groups were analyzed from their differences. Through receiver operating characteristic (ROC) curve analysis, their diagnostic threshold values were calculated, at the same time, SVM model was built.

RESULTS

There was a significant difference between serum index levels of recurrence group and non-recurrence group in one week and six months after operation (P< 0.05); SVM model was established with an accuracy of 96.67% (29/30), a sensitivity of 90% (9/10) and a specificity of 100% (20/20).

CONCLUSIONS

Serum CAl53, CEA and CA125 levels after operation have certain instructional significance for prognosis of breast cancer patients, and the established SVM model has high clinical application value.