How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models

1. Bubendorf, L., Schöpfer, A., Wagner, U., Sauter, G., Moch, H., Willi, N., Gasser, T.C., Mihatsch, M.J.: Metastatic patterns of prostate cancer: an autopsy study of 1,589 patients. Human pathology 31(5), 578–583 (2000)

   Article  Google Scholar 

2. Duffy, M.J.: Biomarkers for prostate cancer: prostate-specific antigen and beyond. Clinical Chemistry and Laboratory Medicine (CCLM) 58(3), 326–339 (2020)

   Article  Google Scholar 

3. Dzikunu, O., Ahamed, S., Toosi, A., Harsini, S., Benard, F., Rahmim, A., Uribe, C.: A 3d unet for automated metastatic lesions detection and segmentation from psma-pet images of patients with biochemical recurrence prostate cancer (2024)

   Google Scholar 

4. Fendler, W.P., Eiber, M., Beheshti, M., Bomanji, J., Ceci, F., Cho, S., Giesel, F., Haberkorn, U., Hope, T.A., Kopka, K., et al.: 68 ga-psma pet/ct: Joint eanm and snmmi procedure guideline for prostate cancer imaging: version 1.0. European journal of nuclear medicine and molecular imaging 44, 1014–1024 (2017)

   Google Scholar 

5. Freedland, S.J., Presti Jr, J.C., Amling, C.L., Kane, C.J., Aronson, W.J., Dorey, F., Terris, M.K., Group, S.D.S., et al.: Time trends in biochemical recurrence after radical prostatectomy: results of the search database. Urology 61(4), 736–741 (2003)

   Google Scholar 

6. Harsini, S., Wilson, D., Saprunoff, H., Allan, H., Gleave, M., Goldenberg, L., Chi, K.N., Kim-Sing, C., Tyldesley, S., Bénard, F.: Outcome of patients with biochemical recurrence of prostate cancer after psma pet/ct-directed radiotherapy or surgery without systemic therapy. Cancer Imaging 23(1),  27 (2023)

   Article  Google Scholar 

7. Haupt, F., Dijkstra, L., Alberts, I., Sachpekidis, C., Fech, V., Boxler, S., Gross, T., Holland-Letz, T., Zacho, H.D., Haberkorn, U., et al.: 68 ga-psma-11 pet/ct in patients with recurrent prostate cancer-a modified protocol compared with the common protocol. European journal of nuclear medicine and molecular imaging 47, 624–631 (2020)

   Article  Google Scholar 

8. Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33, 6840–6851 (2020)

   Google Scholar 

9. Hudson, H.M., Larkin, R.S.: Accelerated image reconstruction using ordered subsets of projection data. IEEE transactions on medical imaging 13(4), 601–609 (1994)

   Article  Google Scholar 

10. Jafari, E., Zarei, A., Dadgar, H., Keshavarz, A., Manafi-Farid, R., Rostami, H., Assadi, M.: A convolutional neural network–based system for fully automatic segmentation of whole-body [68ga] ga-psma pet images in prostate cancer. European Journal of Nuclear Medicine and Molecular Imaging pp. 1–12 (2023)

    Google Scholar 

11. Kostyszyn, D., Fechter, T., Bartl, N., Grosu, A.L., Gratzke, C., Sigle, A., Mix, M., Ruf, J., Fassbender, T.F., Kiefer, S., et al.: Intraprostatic tumor segmentation on psma pet images in patients with primary prostate cancer with a convolutional neural network. Journal of Nuclear Medicine 62(6), 823–828 (2021)

    Article  Google Scholar 

12. Ma, K., Harmon, S.A., Klyuzhin, I.S., Rahmim, A., Turkbey, B.: Clinical application of artificial intelligence in positron emission tomography: Imaging of prostate cancer. PET clinics 17(1), 137–143 (2022)

    Article  Google Scholar 

13. Moon, T.K.: The expectation-maximization algorithm. IEEE Signal processing magazine 13(6), 47–60 (1996)

    Article  Google Scholar 

14. Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. pp. 8162–8171. PMLR (2021)

    Google Scholar 

15. Polson, L., Fedrigo, R., Li, C., Sabouri, M., Dzikunu, O., Ahamed, S., Rahmim, A., Uribe, C.: Pytomography: A python library for quantitative medical image reconstruction. arXiv preprint arXiv:2309.01977 (2023)

16. Rousseau, E., Wilson, D., Lacroix-Poisson, F., Krauze, A., Chi, K., Gleave, M., McKenzie, M., Tyldesley, S., Goldenberg, S.L., Bénard, F.: A prospective study on 18f-dcfpyl psma pet/ct imaging in biochemical recurrence of prostate cancer. Journal of Nuclear Medicine 60(11), 1587–1593 (2019)

    Article  Google Scholar 

17. Soldatov, A., von Klot, C.A., Walacides, D., Derlin, T., Bengel, F.M., Ross, T.L., Wester, H.J., Derlin, K., Kuczyk, M.A., Christiansen, H., et al.: Patterns of progression after 68ga-psma-ligand pet/ct-guided radiation therapy for recurrent prostate cancer. International Journal of Radiation Oncology* Biology* Physics 103(1), 95–104 (2019)

    Google Scholar 

18. Toosi, A., Harsini, S., Ahamed, S., Yousefirizi, F., Bénard, F., Uribe, C., Rahmim, A.: State-of-the-art object detection algorithms for small lesion detection in psma pet: use of rotational maximum intensity projection (mip) images. In: Medical Imaging 2023: Image Processing. vol. 12464, pp. 771–778. SPIE (2023)

    Google Scholar 

19. Toosi, A., Harsini, S., Benard, F., Uribe, C., Rahmim, A.: Advanced deep learning-based lesion detection on rotational 2d maximum intensity projection (mip) images coupled with reverse mapping to the 3d pet domain (2023)

    Google Scholar 

20. Wolleb, J., Sandkühler, R., Bieder, F., Valmaggia, P., Cattin, P.C.: Diffusion models for implicit image segmentation ensembles. In: International Conference on Medical Imaging with Deep Learning. pp. 1336–1348. PMLR (2022)

    Google Scholar 

21. Xu, Y., Klyuzhin, I., Harsini, S., Ortiz, A., Zhang, S., Bénard, F., Dodhia, R., Uribe, C.F., Rahmim, A., Ferres, J.L.: Automatic segmentation of prostate cancer metastases in psma pet/ct images using deep neural networks with weighted batch-wise dice loss. Computers in Biology and Medicine 158, 106882 (2023)

    Article  Google Scholar 

Download references