Farm Credit Competencies Needed and Possessed by Selected Nebraska Young Farmers/Ranchers

Agriculture has changed a great deal, particularly during the past five years. Lower prices for commodities and higher production costs, including higher interest rates, have made it necessary for farmers to become better financial managers. The farm of the future will be treated financially like any other business--it will have to demonstrate profitability before a bank will finance its operations (Congress of the United States, 1986). The Iowa Department of Agriculture (1985) and others concluded that rapid economic and social change is not a new phenomenon in agriculture. Agriculture has in fact been adjusting to conditions of greater efficiency since the beginning of recorded history. What is occurring in agriculture at the present time (farms failing because equity is exhausted or operating credit is denied), has little to do with efficiency but rather the amount of debt that is held is excessive as measured by the economic environment of the 1980\u27s. Harl (1985) expanded upon the present crisis by addressing the massive adjustment taking place in agriculture and the increased demand for educational services for adults remaining in farming. He feels strongly about the heavy emphasis needed in the areas of management skills, cost structure, financial management, financing arrangements, utilization of non-farm sourced equity capital and marketing skills. The investigator found no current studies that had researched the financial management competencies needed to succeed in agriculture. Additionally, the review of literature revealed no current studies that dealt with the specific competencies required for the successful use of farm credit. This lack of research, in combination with the current ongoing farm credit crisis, magnifies the need for a study in this area

Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

Purpose Accurate segmentation of lung nodules is crucial in the development of imaging biomarkers for predicting malignancy of the nodules. Manual segmentation is time consuming and affected by inter-observer variability. We evaluated the robustness and accuracy of a publically available semiautomatic segmentation algorithm that is implemented in the 3D Slicer Chest Imaging Platform (CIP) and compared it with the performance of manual segmentation. Methods: CT images of 354 manually segmented nodules were downloaded from the LIDC database. Four radiologists performed the manual segmentation and assessed various nodule characteristics. The semiautomatic CIP segmentation was initialized using the centroid of the manual segmentations, thereby generating four contours for each nodule. The robustness of both segmentation methods was assessed using the region of uncertainty (δ) and Dice similarity index (DSI). The robustness of the segmentation methods was compared using the Wilcoxon-signed rank test (pWilcoxon<0.05). The Dice similarity index (DSIAgree) between the manual and CIP segmentations was computed to estimate the accuracy of the semiautomatic contours. Results: The median computational time of the CIP segmentation was 10 s. The median CIP and manually segmented volumes were 477 ml and 309 ml, respectively. CIP segmentations were significantly more robust than manual segmentations (median δCIP = 14ml, median dsiCIP = 99% vs. median δmanual = 222ml, median dsimanual = 82%) with pWilcoxon~10−16. The agreement between CIP and manual segmentations had a median DSIAgree of 60%. While 13% (47/354) of the nodules did not require any manual adjustment, minor to substantial manual adjustments were needed for 87% (305/354) of the nodules. CIP segmentations were observed to perform poorly (median DSIAgree≈50%) for non-/sub-solid nodules with subtle appearances and poorly defined boundaries. Conclusion: Semi-automatic CIP segmentation can potentially reduce the physician workload for 13% of nodules owing to its computational efficiency and superior stability compared to manual segmentation. Although manual adjustment is needed for many cases, CIP segmentation provides a preliminary contour for physicians as a starting point

Brief descriptions of five algorithms for ground glass opacity (GGO) or partly solid nodule segmentation.

<p>Brief descriptions of five algorithms for ground glass opacity (GGO) or partly solid nodule segmentation.</p

Robustness (or stability) of the manual and CIP-based segmentation.

<p>The robustness of the manual and CIP-based segmentation assessed with the region of uncertainty (δ) and Dice similarity index (dsi).</p

Comparison of manual (left) and CIP-based (right) segmentation.

<p>Yellow shaded region indicated the disagreement (or region of uncertainty) between contours performed by four radiologists (bottom left) or different CIP-based seed locations (bottom right). In this example, the region of uncertainty for manual segmentation was 3222 ml while the region was only 46 ml for the CIP-based segmentation. dsi_CIP was ≈ 100%, while dsi_manual was 88%.</p