| Title |
Computer-assisted liver tumor surgery using a novel semiautomatic and a hybrid semiautomatic segmentation algorithm
|
|---|---|
| Published in |
Medical & Biological Engineering & Computing, August 2015
|
| DOI | 10.1007/s11517-015-1369-5 |
| Pubmed ID | |
| Authors |
Apollon Zygomalas, Dionissios Karavias, Dimitrios Koutsouris, Ioannis Maroulis, Dimitrios D. Karavias, Konstantinos Giokas, Vasileios Megalooikonomou |
| Abstract |
We developed a medical image segmentation and preoperative planning application which implements a semiautomatic and a hybrid semiautomatic liver segmentation algorithm. The aim of this study was to evaluate the feasibility of computer-assisted liver tumor surgery using these algorithms which are based on thresholding by pixel intensity value from initial seed points. A random sample of 12 patients undergoing elective high-risk hepatectomies at our institution was prospectively selected to undergo computer-assisted surgery using our algorithms (June 2013-July 2014). Quantitative and qualitative evaluation was performed. The average computer analysis time (segmentation, resection planning, volumetry, visualization) was 45 min/dataset. The runtime for the semiautomatic algorithm was <0.2 s/slice. Liver volumetric segmentation using the hybrid method was achieved in 12.9 s/dataset (SD ± 6.14). Mean similarity index was 96.2 % (SD ± 1.6). The future liver remnant volume calculated by the application showed a correlation of 0.99 to that calculated using manual boundary tracing. The 3D liver models and the virtual liver resections had an acceptable coincidence with the real intraoperative findings. The patient-specific 3D models produced using our semiautomatic and hybrid semiautomatic segmentation algorithms proved to be accurate for the preoperative planning in liver tumor surgery and effectively enhanced the intraoperative medical image guidance. |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 35 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 23% |
| Researcher | 5 | 14% |
| Other | 4 | 11% |
| Student > Master | 4 | 11% |
| Student > Postgraduate | 3 | 9% |
| Other | 5 | 14% |
| Unknown | 6 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 31% |
| Computer Science | 5 | 14% |
| Engineering | 5 | 14% |
| Nursing and Health Professions | 2 | 6% |
| Social Sciences | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 10 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 April 2016.
All research outputs
#22,759,802
of 25,374,917 outputs
Outputs from Medical & Biological Engineering & Computing
#1,899
of 2,053 outputs
Outputs of similar age
#239,322
of 279,024 outputs
Outputs of similar age from Medical & Biological Engineering & Computing
#20
of 25 outputs
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