Advanced image processing methods for automatic liver segmentation

This paper presents advanced methods of image segmentation suitable for automatic recognition of the human liver and its vessel system, but in general could be used to segment any organ or body tissue. The comparison of studied methods is being made in terms of segmentation quality and algorithm speed. The main criterion for quality evaluation of each selected method is the level of conformity between the automatically recognized boundary and the reference boundary specified by experienced user. For all the tests sequences of CT and MRI images were used

Advanced image processing methods for automatic liver segmentation

This paper presents advanced methods of image segmentation suitable for automatic recognition of the human liver and its vessel system, but in general could be used to segment any organ or body tissue. The comparison of studied methods is being made in terms of segmentation quality and algorithm speed. The main criterion for quality evaluation of each selected method is the level of conformity between the automatically recognized boundary and the reference boundary specified by experienced user. For all the tests sequences of CT and MRI images were used

A single reference measurement can predict liver tumor motion during respiration

AimTo evaluate liver tumor motion and how well reference measurement predicts motion during treatment.Material and methodsThis retrospective study included 20 patients with colorectal cancer that had metastasized to the liver who were treated with stereotactic ablative radiotherapy. An online respiratory tumor tracking system was used. Tumor motion amplitudes in the superior-inferior (SI), latero-lateral (LL), and anterior-posterior (AP) directions were collected to generate patient-specific margins. Reference margins were generated as the mean motion and 95th percentile of motion from measurements recorded for different lengths of time (1, 3, and 5[[ce:hsp sp="0.25"/]]min). We analyzed the predictability of tumor motion in each axis, based on the reference measurement and intra-/interfraction motions.ResultsAbout 96,000 amplitudes were analyzed. The mean tumor motions were 9.9[[ce:hsp sp="0.25"/]]±[[ce:hsp sp="0.25"/]]4.2[[ce:hsp sp="0.25"/]]mm, 2.6[[ce:hsp sp="0.25"/]]±[[ce:hsp sp="0.25"/]]0.8[[ce:hsp sp="0.25"/]]mm, and 4.5[[ce:hsp sp="0.25"/]]±[[ce:hsp sp="0.25"/]]1.8[[ce:hsp sp="0.25"/]]mm in the SI, LL, and AP directions, respectively. The intrafraction variations were 3.5[[ce:hsp sp="0.25"/]]±[[ce:hsp sp="0.25"/]]1.8[[ce:hsp sp="0.25"/]]mm, 0.63[[ce:hsp sp="0.25"/]]±[[ce:hsp sp="0.25"/]]0.35[[ce:hsp sp="0.25"/]]mm, and 1.4[[ce:hsp sp="0.25"/]]±[[ce:hsp sp="0.25"/]]0.65[[ce:hsp sp="0.25"/]]mm for the SI, LL, and AP directions, respectively. The interfraction motion variations were 1.32[[ce:hsp sp="0.25"/]]±[[ce:hsp sp="0.25"/]]0.79[[ce:hsp sp="0.25"/]]mm, 0.31[[ce:hsp sp="0.25"/]]±[[ce:hsp sp="0.25"/]]0.23[[ce:hsp sp="0.25"/]]mm, and 0.68[[ce:hsp sp="0.25"/]]±[[ce:hsp sp="0.25"/]]0.62[[ce:hsp sp="0.25"/]]mm for the SI, LL, and AP directions, respectively. The Pearson's correlation coefficients for margins based on the reference measurement (mean motion or 95th percentile) and margins covering 95% of the motion during the whole treatment were 0.8–0.91, 0.57–0.7, and 0.77–0.82 in the SI, LL, and AP directions, respectively.ConclusionLiver tumor motion in the SI direction can be adequately represented by the mean tumor motion amplitude generated from a single 1[[ce:hsp sp="0.25"/]]min reference measurement. Longer reference measurements did not improve results for patients who were well-educated about the importance of regular breathing. Although the study was based on tumor tracking data, the results are useful for ITV delineation when tumor tracking is not available