New discoveries of image display size on observer performance.
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Algorithms↗
Biomedical subjects
Jean Gao
Publications and source records attributed to Jean Gao.
Defining vascular supply and territory of thinned perforator flaps: Part II. Superior gluteal artery perforator flap.
BACKGROUND: Superior gluteal artery perforator flaps are surgical options in breast and pressure sore reconstructions. Based on the recipient site, primary thinning of these flaps may be necessary for final optimal contour. As the thinning of a superior gluteal artery perforator flap should be based on the knowledge of perforator vascular territories to prevent vascular compromise, the authors performed an anatomical study to determine the number, location, and diameter of the perforators present in the superior gluteal artery perforator flap. Accompanying veins and acceptable locations for surgical incisions were also determined. METHODS: Fourteen superior gluteal artery perforator flaps were harvested from seven cadavers. Perforator flaps were thinned to 8 to 15 mm, except for a 2.5-cm radius around the dissected perforator. Vascular territory areas were quantified before and after thinning by photographic and radiographic methods, and respective vascular territory maps were constructed. Surgical incision "danger zones" of vertical and horizontal axes were determined at specific depths (relative to the skin surface) for each flap. Danger zone measurements were determined with an automatic three-dimensional vascular tree construction using computed tomographic images and several modeling algorithms. RESULTS: Mean perforator artery diameter and number at the fascia level were 0.91 +/- 0.07 mm and 2.86 +/- 0.77 (mean +/- SD), respectively. Perforator pedicles were located midway between the posterior superior iliac spine and the greater trochanter. After thinning, skin surface and whole flap vascular territories were reduced 80.9 percent (photographic) and 76.9 percent (radiographic), respectively, compared with unthinned vascular territory areas. From the skin at 4-, 6-, and 8-mm thicknesses, elliptical danger zones (two vertical segments and two horizontal segments) had overall vertical segment axis length ranges from the pedicles of 59 to 66 mm, 51 to 57 mm, and 49 to 51 mm, respectively. Horizontal axis segment length ranges were 61 to 76 mm, 61 to 66 mm, and 60 to 57 mm for 4-, 6-, and 8-mm skin thicknesses, respectively. CONCLUSIONS: The superior gluteal artery perforator flap provides an excellent blood supply to adipose tissue but may be compromised when aggressively thinned. Surgeons may design and harvest partially thinned superior gluteal artery perforator flaps based on the anatomical vascular territory maps provided by this study.
Angiography↗
A deformable model for automatic CT liver extraction.
RATIONALE AND OBJECTIVES: This study was performed to design an automatic liver region extraction system to facilitate clinical liver size estimation and further serve as a prestage for liver reconstruction and volume estimation. MATERIALS AND METHODS: We present a modification of the well-known snakes algorithm for extracting liver regions in noisy CT images. Our modification addresses the issues of selection of the control points on an estimate of the contour and the determination of the weighting coefficients. The weighting coefficients are determined dynamically on the basis of the distance between the control points and the local curvature of the contour. RESULTS: The proposed method was used in extracting liver regions from 98 cross-sectional abdominal images. The overall performance was estimated by comparisons with original liver regions. CONCLUSION: The deformable model method enables an efficient and effective automatic liver region extraction in noisy environments. This approach eliminates human-in-the loop, which is the common practice for the majority of current methods.
Algorithms↗
Diagnosis of early relapse in ovarian cancer using serum proteomic profiling.
Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers to help early detection of the disease. Ovarian cancer commonly recurs at the rate of 75% within a few months or several years later after standard treatment. Since recurrent ovarian cancer is relatively difficult to be diagnosed and small tumors generally respond better to treatment, new methods for the detection of early relapse in ovarian cancer are urgently needed. Here, we propose a new algorithm SVM-MB/RFE (SVM-Markov Blanket/Recursive Feature Elimination) based on SVM-RFE, which identifies biomarkers for predicting the early recurrence of ovarian cancer. In this approach, we first apply t-test for feature pruning and then binning using 5-fold cross validation. Finally, 58 peaks are obtained from 27,000 of the raw data. Such dramatically reduced features relax the computational burden in the next step of our algorithm. We compare the performance of three feature selection algorithms and demonstrate that SVM-MB/RFE outperforms other methods.
Algorithms↗