ABSTRACT
Purpose: Repetitive transcranial magnetic stimulation (rTMS) is an effective therapy in improving depressive symptoms in MDD patients, but the intrinsic mechanism is still unclear. In this study, we investigated the influence of rTMS on brain gray matter volume for alleviating depressive symptoms in MDD patients using structural magnetic resonance imaging (sMRI) data. Methods: Patients with first episode, unmedicated patients with MDD (n = 26), and healthy controls (n = 31) were selected for this study. Depressive symptoms were assessed before and after treatment by using the HAMD-17 score. High-frequency rTMS treatment was conducted in patients with MDD over 15 days. The rTMS treatment target is located at the F3 point of the left dorsolateral prefrontal cortex. Structural magnetic resonance imaging (sMRI) data were collected before and after treatment to compare the changes in brain gray matter volume. Results: Before treatment, patients with MDD had significantly reduced gray matter volumes in the right fusiform gyrus, left and right inferior frontal gyrus (triangular part), left inferior frontal gyrus (orbital part), left parahippocampal gyrus, left thalamus, right precuneus, right calcarine fissure, and right median cingulate gyrus compared with healthy controls (P < 0.05). After rTMS treatment, significant growth in gray matter volume of the bilateral thalamus was observed in depressed patients (P < 0.05). Conclusion: Bilateral thalamic gray matter volumes were enlarged in the thalamus of MDD patients after rTMS treatment and may be the underlying neural mechanism for the treatment of rTMS on depression.
ABSTRACT
PURPOSE: Accurately detecting and removing pectoral muscle areas depicting on mediolateral oblique (MLO) view mammograms are an important step to develop a computer-aided detection scheme to assess global mammographic density or tissue patterns. This study aims to develop and test a new fully automated, accurate and robust method for segmenting pectoral muscle in MLO mammograms. METHODS: The new method includes the following steps. First, a small rectangular region in the top-left corner of the MLO mammogram which may contain pectoral muscle is captured and enhanced by the fractional differential method. Next, an improved iterative threshold method is applied to segment a rough binary boundary of the pectoral muscle in the small region. Then, a rough contour is fitted with the least squares method on the basis of points of the rough boundary. Last, the fitting contour is subjected to local active contour evolution to obtain the final pectoral muscle segmentation line. The method has been tested on 720 MLO mammograms. RESULTS: The segmentation results generated using the new scheme were evaluated by two expert mammographic radiologists using a 5-scale rating system. More than 65% were rated above scale 3. When assessing the segmentation results generated using Hough transform, morphologic thresholding methods and Unet-based model, less than 20%, 35% and 47% of segmentation results were rated above scale 3 by two radiologists, respectively. Quantitative data analysis results show that the Dice coefficient of 0.986 ± 0.005 is obtained. In addition, the mean rate of errors and Hausdorff distance between the contours detected by automated and manual segmentation are FP = 1.71 ± 3.82%, FN = 5.20 ± 3.94% and 2.75 ± 1.39 mm separately. CONCLUSION: The proposed method can be used to segment the pectoral muscle in MLO mammograms with higher accuracy and robustness.