Fully Automatic Segmentations of Liver and Hepatic Tumors From 3-D Computed Tomography Abdominal Images: Comparative Evaluation of Two Automatic Methods

Theranostic applications: Non-ionizing cellular and molecular imaging through innovative nanosystems for early diagnosis and therapy

“…These activities, whose final goal is the introduction of novel combined therapies by means of targeting, drug and gene delivery, have been performed, including custom designed phantoms for "in vitro" studies, as well as "ex vivo" experimentations and "in vivo" trials in animal models [13][14][15] ; (2) automatic information extraction from biomedical images and signals for anatomical and functional investigations: automatic image segmentation and registration, tissue characterization (virtual biopsy), volume rendering for augmented and virtual reality applied to oncology radiotherapy and minimally invasive therapies (operation planning, intraoperative image guidance, training, etc. ), functional MRI studies, employment of neural networks and expert systems for supporting medical decisions [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] (Figure 3); and (3) industrial research for the design and development of new systems and tools with the related intellectual property protection and patenting activities at a national and international level, bridging the gap between applied research and industries in this field.…”

Segmentation of Liver from Abdomen CT images and 3D Visualization

“…Other limitation is that in some cases the boundary between liver and neighboring organs disappears and it becomes hard to determine the difference among different organs and this leads to erroneous segmentation. Casciaro et al gives an adaptive initialization method to produce fully automatic processing frameworks based on graph-cut and gradient flow active contour algorithms [9]. They used mean shift filter for pre-processing because mean shift filter preserve edges and does not blur edges.…”

Automated segmentation methods for liver analysis in oncology applications

“… Casciaro et al [82] published a method that could achieve TPR=92.3% (no information about FPC).  The evaluation of the method presented by Linguraru et al [83] demonstrated TPR=100% with FPC=2.3.…”

“…In addition to the main cancer types the detection and segmentation of brain pathologies [66] in MR images [67], lymph nodes in CT images [68], and liver tumours in CT or MR images were also focused on in many publications. The three main motivations for liver lesion detection are lesion classification [51, 69-73, 88, 90], lesion segmentation and quantification [75][76][77][78][79][80][81][82][83][84][85]89], and follow-up [86]. The following paragraphs summarize the recent methods and results related to liver lesion detection.…”

“…Masuda et al [81] proposed an algorithm that classifies voxels into normal and abnormal classes using the expectationmaximization method, and performs a morphological filter that incorporates circularity and proximity to boundary to eliminate false positives. The method presented by Casciaro et al [82] is based on graph-cut and gradient flow active contour, which has a relatively high computation cost (10+ seconds per slice). Linguraru et al [83] published a technique that uses graph cuts to segment the hepatic tumours using shape and enhancement constraints.…”