By Hidetaka Arimura
This e-book offers a entire evaluation of the state of the art computational intelligence learn and applied sciences in computer-assisted radiation treatment in keeping with picture engineering. It additionally strains significant technical developments and examine findings within the box of image-based computer-assisted radiation therapy.
In high-precision radiation cures, novel methods in snapshot engineering together with special effects, photograph processing, development reputation, and computational anatomy play vital roles in enhancing the accuracy of radiation remedy and supporting choice making by means of radiation oncology pros, comparable to radiation oncologists, radiation technologists, and scientific physicists, in each one section of radiation therapy.
All the subjects offered during this e-book expand realizing of the fashionable scientific applied sciences and platforms for image-based computer-assisted radiation remedy. for this reason this quantity will significantly profit not just radiation oncologists and radiologists but in addition radiation technologists, professors in scientific physics or engineering, and engineers all for the improvement of goods to make use of this complex therapy.
Read or Download Image-Based Computer-Assisted Radiation Therapy PDF
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Extra resources for Image-Based Computer-Assisted Radiation Therapy
Example text
Consequently, nodules are more continuous in MDCT volumes. To process 3D MDCT volumes effectively, the development of a 3D technique for the reduction of FPs is necessary. Therefore, we developed a CAD scheme for nodule detection for thin-slice CT, which consisted of the detection of initial nodule candidates based on a selective enhancement filter (Li et al. 2003) and classification of the nodule candidates into nodules and non-nodules based on a rule-based 34 K. Suzuki scheme with image features.
1 Distinction between nodules and non-nodules Scoring with s2 Nodule (ROI) No. N MTANN Fig. 8 Architecture of a mixture of expert MTANNs consisting of multiple MTANNs combined by the integration ANN with the same nodules. , MTANN No. 1 is trained to distinguish nodules from false positives caused by medium-sized vessels; MTANN No. 2 is trained to distinguish nodules from soft-tissue-opacity false 28 K. Suzuki positives caused by the diaphragm; and so on. A scoring method is applied to the output of each MTANN, and then thresholding of the score from each MTANN is performed for distinction between nodules and the specific type of non-nodule.
Non-nodules for the training of MTANN No. 1 to No. 5 ranged from medium-sized vessels to small (peripheral) vessels. Non-nodules for the training of MTANN No. 6 to No. 9 were large vessels in the hilum, relatively large vessels with some opacities, soft-tissue opacities caused by the partial volume effect between peripheral vessels and the 30 K. Suzuki diaphragm, and some abnormal opacities (focal interstitial opacities), respectively. Each MTANN was trained in the same way as a single MTANN. The trained MTANN was applied to 1068 false-positive nodule candidates not used for training.