Proposal of computer-aided detection system for three dimensional CT images of liver cancer

Shimizu, A.; Kawamura, Takuto; Kobatake, Hidefumi

doi:10.1016/j.ics.2005.03.070

Cited by 9 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A lot of work has been done in the context of lung nodules CAD over the last years and recently first research reports appeared for liver lesions as well [11]. But the main problem would be the detection of enlarged lymph nodes, which seems to be an unsolvable task-based only on CT images-considering that enlarged lymph nodes can develop nearly everywhere and considering how much other structures of similar density could be connected to them.…”

Section: Discussionmentioning

confidence: 99%

OncoTREAT: a software assistant for cancer therapy monitoring

et al. 2006

View full text Add to dashboard Cite

Object Cancer is one of the leading causes of death worldwide and therapy options are often associated with severe stress for the patient and high costs. Therefore, precise evaluation of therapy success is essential. Material and methods In the framework of the VICORA research project (Virtual Institute for Computer Assistance in Clinical Radiology), a software application L. Bornemann (B) · was developed to support the radiologist in evaluating the response to tumor therapy. The application provides follow-up support for oncological therapy monitoring by volumetric quantification of lung, liver and brain metastases as well as enlarged lymph nodes and assists the user by temporal registration of lesion positions. Results With close cooperation between computer scientists and radiologists the application was tested and optimized to achieve a high degree of usability. Several clinical studies were carried out to evaluate the robustness and reproducibility of the volumetry methods. Conclusion Automatic volumetry and segmentation allows reliable detection of tumor growth and has the potential to increase reliability and significance of monitoring tumor growth in follow-up examinations.

show abstract

Section: Discussionmentioning

confidence: 99%

OncoTREAT: a software assistant for cancer therapy monitoring

et al. 2006

View full text Add to dashboard Cite

show abstract

“…To segment the aforementioned clinical cases, we applied a previously investigated procedure [15][16][17][18]. Finally, a 3D model of a liver tumor case was constructed using the commercial solution Synapse 3D, as in Lo Presti et al, Takahashi, Akinari Miyazaki et al, and Yukio Oshiro et al [19][20][21][22]. The volumetric models were imported into the OsiriX platform and fused with the matching radiological images.…”

Section: Converting the Segmentationmentioning

confidence: 99%

Assessment of DICOM Viewers Capable of Loading Patient-specific 3D Models Obtained by Different Segmentation Platforms in the Operating Room

et al. 2015

View full text Add to dashboard Cite

Patient-specific 3D models obtained by the segmentation of volumetric diagnostic images play an increasingly important role in surgical planning. Surgeons use the virtual models reconstructed through segmentation to plan challenging surgeries. Many solutions exist for the different anatomical districts and surgical interventions. The possibility to bring the 3D virtual reconstructions with native radiological images in the operating room is essential for fostering the use of intraoperative planning. To the best of our knowledge, current DICOM viewers are not able to simultaneously connect to the picture archiving and communication system (PACS) and import 3D models generated by external platforms to allow a straight integration in the operating room. A total of 26 DICOM viewers were evaluated: 22 open source and four commercial. Two DICOM viewers can connect to PACS and import segmentations achieved by other applications: Synapse 3D® by Fujifilm and OsiriX by University of Geneva. We developed a software network that converts diffuse visual tool kit (VTK) format 3D model segmentations, obtained by any software platform, to a DICOM format that can be displayed using OsiriX or Synapse 3D. Both OsiriX and Synapse 3D were suitable for our purposes and had comparable performance. Although Synapse 3D loads native images and segmentations faster, the main benefits of OsiriX are its userfriendly loading of elaborated images and it being both free of charge and open source.

show abstract

“…Features in the second group are based on the output of a convergence index (C.I.) filter [3] used for enhancing tumours. The spatial filter computes a convergence index of gradient vectors in a spherical mask region centred at the voxel of interest, resulting in a high convergence index around the centre of the tumour.…”

Section: An Ensemble Learning Algorithmmentioning

confidence: 99%

“…The matched filter is designed in terms of the CT value profile of the tumour whose size is estimated by a C.I. filter [3]. AdaBoost, MadaBoost and MBRη-Boost comprise a class of U-Boost; each has a different loss function U(z), as shown in Table 2, resulting in different properties against outliers and mislabelling of liver tumour labels.…”

Section: An Ensemble Learning Algorithmmentioning

confidence: 99%