Title: A new PC based software to take and validate clinical decisions for colorectal cancer using metric 3D images segmentations. Authors: Angel Osorio, Julien Nauroy, Sonia Dahdouh, Jean-Marie Biset, Roland Boustani, Patricia Donars. Learning Objectives: 1. - Clinical decisions in colorectal cancer require the patient’s consent. Therefore the latter should be clearly informed. The overall clinical diagnosis is used to provide a fully-informed consent for surgery. 2. - A specific 3D segmentation system computes volumes then displays images understandable by non medical people. This system can also measure positions and sizes of organs and lesions. Radiological data are provided by CT, MR or US examinations [1][2]. 3. - Comparison of the patient’s images to standard data can give evidence for surgical decisions. This information may be used if legal problems arise after surgery, mainly concerning anastomoses. Background: Diagnosis of low-sigmoid carcinoma often starts with a CT scan or MR examination, the diagnosis being assessed with colonoscopies and rectal ultrasound imaging. Even if the CT interpretation workstations often display 3D reconstructions, they are global complex images which are difficult to interpret for non medical people. We need simple images which include only the interesting organs and lesions concerning the pathology. Procedure Details: We have developed a new software to segment and visualize 3D reconstructions of the regions of interest, mainly sigmoid and colorectal carcinomas. The software developed (PTM3D as “Poste de travail medical 3D” or 3D medical workstation) [3] is oriented towards interactive segmentation of organs and lesions. It uses DICOM data and includes a 3D display unit and 3D navigation functions (figure 1).

Figure1. 3D display and navigation interface including a tilted plane. © A. Osorio, LIMSI-CNRS, Orsay, France

Two main 3D segmentations methods are included: Marching cubes [4] and active contours [5]. 3D segmentations using marching cubes: The user surrounds the region of interest using a parallelepipedic or spherical envelop and he points an external point to this area. The software proposes a segmented volume which can be edited (figure 2).

Figure 2. 3D segmentation using marching cubes method and parallelepipedic envelop © A. Osorio, LIMSI-CNRS, Orsay, France

3D segmentations using active contours: Clicking in an inner point of a slice in the ROI, an initial contour is proposed (figure 3). If accepted by the user (figure 4) the system automatically reconstructs, slice by slice, the global contour (figure 5) and then it tessellates and displays the volume (figure 6). In the case of low contrast in lesion boundaries, as it often happens in colorectal carcinomas, the user can edit the contour mesh points (figure 7).

Figure 3. Semiautomatic initial contour for active contours method. © A. Osorio, LIMSI-CNRS, Orsay, France

Figure 4. Initial contour evolution and acceptation. © A. Osorio, LIMSI-CNRS, Orsay, France

Figure 5. Global contours including mesh points. © A. Osorio, LIMSI-CNRS, Orsay, France

Figure 6. 3D segmentation of a colorectal carcinoma including rectum. © A. Osorio, LIMSI-CNRS, Orsay, France

Figure 7. Mesh points can be edited to fit edges in poor contrast images. © A. Osorio, LIMSI-CNRS, Orsay, France

All geometrical and physical characteristics of volumes are saved and displayed, including colours, transparencies and intersections with other volumes (figure 8). Sizes and distances can be measured and displayed. The system shows on a graphical map the standard values and a comparison with the patient's parameters. This approach is better than the classical examination's global 3D display [6]. Once the images are obtained, the measurements of lesions and their precise anatomic locations are used as evidence for patients and they can be used as an information tool for oncologic surgery (figure 9). In case of legal problems, the information provided can be easily interpreted and the decision justified.

Figure 8. 3D display of rectum-adenocarcinoma, coccyx and patient’s body using transparencies.

© A. Osorio, LIMSI-CNRS, Orsay, France

Figure 9. 3D segmentation of coccyx, rectum, adenocarcinoma and bladder. Adenocarcinoma volume is displayed.

© A. Osorio, LIMSI-CNRS, Orsay, France

Results: The software runs on any standard PC under any Windows version. Segmentations are carried out in less than a minute. This system has been used in 27 questionable cases, treated in the last 18 months. The images were used to take surgical decisions, get a fully-informed consent from the patient and as evidence that the clinical decision was appropriate. Furthermore a good knowledge of organs and lesions sizes and positions can avoid future complications [7]. In the figures 10 and 11, we present 3D segmentation using MR examination in a colorectal carcinoma case. Conclusion: The software is user friendly, it has full help integrated and it can be learned in 1 hour by a new user. All reconstructions and numerical data can be easily printed, exported and included in the patient’s file. Furthermore, the data can be used in the patient’s follow-up knowing the treatments efficiency, mainly in chemotherapy, comparing volumes

measurements in next CT examinations. The results can be also used in the operating room giving precise information for surgical acts and saving intervention time. For years the surgeon was alone in the clinical decision process. Fortunately, the breakthroughs in radiology and endoscopy in the last years lead to a shared responsibility between health professionals. The question is: In 2010, how is it possible for a Surgeon to explain clearly to the patient that, in his case, amputation with final colostomy is the only solution? The answer is probably “using 3D images easily understandable by non professionals”.

Figure 10. 3D segmentation of rectum and colorectal carcinoma using MR axial slices. © A. Osorio, LIMSI-CNRS, Orsay, France

Figure 11. 3D segmentation of rectum and colorectal carcinoma using MR sagittal slices. © A. Osorio, LIMSI-CNRS, Orsay, France

Personal Information: References: [1] Bruel JM., Pierredon MA. IRM et echoendoscopie dans le bilan d’extension du cancer du rectum : complementarite plutôt que competition. Journal de Radiologie, 2007, 88, 1839-41, Elsevier Masson SAS, Paris 2007. [2] Hoeffel C., Marra MD., Azizi L. et al. Bilan preoperatoire des cancers du rectum en IRM pelvienne haute resolution avec antenne en reseau phase. Journal de Radiologie, 2006, 87, 1821-30, Elsevier Masson SAS, Paris 2006. [3] Osorio, A., Merran, S., and Bedelet, O. “PC Based Software for 3D Instant Volumes Measurements on CT Images: Application to Lymph Nodes Measurements in Lymphomas Follow-up”, RSNA 1999, infoRAD, Supplement to Radiology, 1999, 213, 577 and http://perso.limsi.fr/osorio/.

[4] Lopes, A. and Brodlie, K., “Improving the Robustness and Accuracy of the Marching Cubes Algorithm for Isosurfacing”, IEEE Transactions on Visualization and Computer Graphics, 2003, 9(1) 16-29. [5] McInerney, T. and Terzopoulos, D., “T-snakes: Topology adaptative snakes”, Medical Image Analysis, 2000, 4(2), pp 73-91. [6] Osorio, A., Biset, J-M., Boustani, et al, “A new system to validate and help gastroplasties ans cholecystectomies under laparoscopy using 3D segmented radiological images”. International Journal of Computer Assisted Radiology and Surgery, 2007, 2(1), 204-217. [7] Liu L., Herrington LJ., Hornbrook MC., Wendel CS. Et al. Early and late complications among long-term colorectal cancer survivors with ostomy or anastomosis. Dis Colon Rectum. 2010, 53(2), 200-12. Keywords: Colorectal cancer, Surgery, Clinical decisions, Patient’s consent, Easy understandable images.