A hybrid segmentation approach for rapid and reliable liver volumetric analysis in daily clinical practice

التفاصيل البيبلوغرافية
العنوان: A hybrid segmentation approach for rapid and reliable liver volumetric analysis in daily clinical practice
المؤلفون: Karavias D. Dimitrios, Maroulis Ioannis, Giokas Konstantinos, Koutsouris Dimitrios, Zygomalas Apollon, Karavias Dionissios, Megalooikonomou Vasileios
المصدر: BIBE
بيانات النشر: IEEE, 2015.
سنة النشر: 2015
مصطلحات موضوعية: medicine.medical_specialty, Preoperative planning, medicine.diagnostic_test, business.industry, Magnetic resonance imaging, Image segmentation, Liver resections, Hybrid approach, Thresholding, Clinical Practice, medicine, Segmentation, Radiology, business
الوصف: Preoperative evaluation of liver future remnant volume is essential for liver oncologic and transplantation surgery. Segmentation of liver imaging studies allow for an excellent liver volumetric analysis. We developed a hybrid liver segmentation algorithm which is based on thresholding by pixel intensity value. The algorithm consists of a semiautomatic and an automatic part. The aim of this prospective study was to evaluate the efficacy of preoperative liver volumetric analysis in daily clinical practice using this hybrid approach. Accuracy and speed were validated on a random prospectively selected sample of 20 patients undergoing elective major liver resections at our institution from June 2013 to June 2015. Complete liver volumetric analysis was performed in average in 15.5 min/dataset SD±2.6 (computation and interaction time). Mean similarity index was 95.5% SD±2. The future liver remnant volume calculated by the application showed a correlation of 0.98 to that calculated using manual boundary tracing. The hybrid segmentation approach proved to be fast and accurate for the preoperative planning in oncologic liver surgery.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::7cb9062749fa9c8ec77fb8bcea01dc42Test
https://doi.org/10.1109/bibe.2015.7367715Test
رقم الانضمام: edsair.doi...........7cb9062749fa9c8ec77fb8bcea01dc42
قاعدة البيانات: OpenAIRE