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After years of development, the RadLex terminology contains a large set of controlled terms for the radiology domain, but gaps still exist. We developed a data-driven approach to discover new terms for RadLex by mining a large corpus of radiology reports using natural language processing (NLP) methods. Our system, developed for mammography, discovers new candidate terms by analyzing noun phrases in free-text reports to extend the mammography part of RadLex. Our NLP system extracts noun phrases from free-text mammography reports and classifies these noun phrases as "Has Candidate RadLex Term" or "Does Not Have Candidate RadLex Term." We tested the performance of our algorithm using 100 free-text mammography reports. An expert radiologist determined the true positive and true negative RadLex candidate terms. We calculated precision/positive predictive value and recall/sensitivity metrics to judge the system's performance. Finally, to identify new candidate terms for enhancing RadLex, we applied our NLP method to 270,540 free-text mammography reports obtained from three academic institutions. Our method demonstrated precision/positive predictive value of 0.77 (159/206 terms) and a recall/sensitivity of 0.94 (159/170 terms). The overall accuracy of the system is 0.80 (235/293 terms). When we ran our system on the set of 270,540 reports, it found 31,800 unique noun phrases that are potential candidates for RadLex. Our data-driven approach to mining radiology reports can identify new candidate terms for expanding the breast imaging lexicon portion of RadLex and may be a useful approach for discovering new candidate terms from other radiology domains.

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OzetseBu makalede dbrt farkli 3 boyutlu bhlutleme algoritmasi uygulamaya geyirilerek performamlan farkli CT veri kumesi iPzerinde test edilmi'tir. Uygulamaya geyirilen bhlutleme algoritmalari; tohumlu bolge geni4uemesi (seeded region growing), WEIBULL E-SD alardannu kIlanarak 3B bulltleme (volumetric segmentation using WEIBULL E-SD fields), OTSU yontenmini kullanarak otomatik yok seviyeli egikleme (automatic muiltilevel thresholding by using OTSU method) ve tohumsuz bolge genilemesi (useeded region growing) algoritmalandir. AbstractIn this paper we present an evaluation of four different 3D segmentation algorithms with respect to their performance on three different CT Data Sets. The segmentation algorithms evaluated in this study are seeded region growing, volumetric segmentation using WEIBULL E SD fields, automatic multilevel thresholding by using OTSU method and unseeded region growing. The main results gained from our experimentation and implementation details are presented.