Published Papers

2026

  1. Tiihonen, A., Salonen, I., Koivisto, I., Ritamäki, A., Jaatinen, S., Hyvärinen, T., … Hypoxia shapes tumor immune microenvironment through cell-type dependent responses in diffuse astrocytomas. Cancer Research, 86(7_Supplement), 778–778, 2026.
  2. Petäinen, L., Väyrynen, J.P., Böhm, J., Ruusuvuori, P., Ahtiainen, M., Elomaa, H., … Äyrämö, S. dMMR prediction from colorectal cancer histopathology: Leveraging non-tumor and low-magnification regions. Computer Methods and Programs in Biomedicine, 280, 109317, 2026.
  3. Liimatainen, K., Latonen, L., Ruusuvuori, P. SparStVR — exploring sparse 3D histology data in virtual reality. Communications Engineering, 2026.
  4. Khan, U., Härkönen, J., Friman, M., Hakimnejad, H., Latonen, L., Kuopio, T., … Ruusuvuori, P. Staining normalization in histopathology: Method benchmarking using multicenter dataset. Scientific Reports, 2026.

2025

  1. Pouplier, S.S., Sharma, A., Ruusuvuori, P., Hartman, J., Jensen, M.B., Ejlertsen, B., … Validation of a deep learning-based AI model for breast cancer risk stratification in postmenopausal ER+/HER2− breast cancer patients. The Breast, 104671, 2025.
  2. Tiihonen, A.M., Salonen, I., Koivisto, I., Ritamäki, A.S., Jaatinen, S., Hyvärinen, T., … P02.29.B Hypoxia shapes tumor immune microenvironment through cell-type dependent responses in diffuse astrocytomas. Neuro-Oncology, 27(Supplement_3), iii49, 2025.
  3. Hartman, J., Pouplier, S., Sharma, A., Ruusuvuori, P., Jensen, M.B., Ejlertsen, B., … 373P Validation of the deep learning-based AI model for breast cancer risk stratification in postmenopausal ER+ HER2− breast cancer patients. Annals of Oncology, 36, S358, 2025.
  4. Hernández, N., Pakarinen, T., Salminen, A., … Valkonen, M., Ruusuvuori, P., … Arponen, O. A systematic approach to study the effects of acquisition parameters and biological factors on computerized mammography analysis using ex vivo human tissue. PLoS ONE, 20(8), e0321658, 2025.
  5. Välimäki, A., Haapaniemi, T., Valkonen, M., Virtanen, P., Peippo, M., Kujari, H., … Digitalization of pathology in a multicenter setup: A user experience study and comparison of two alternative implementation strategies. Pathology — Research and Practice, 272, 156099, 2025.
  6. Mulliqi, N., Blilie, A., Ji, X., Szolnoky, K., Olsson, H., Titus, M., … Ruusuvuori, P., … Kartasalo, K. Development and retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies: study protocol. BMJ Open, 15(7), e097591, 2025.
  7. Ji, X., Salmon, R., Mulliqi, N., Khan, U., Wang, Y., Blilie, A., Olsson, H., … Ruusuvuori, P., Eklund, M., Kartasalo, K. Physical color calibration of digital pathology scanners for robust AI-assisted cancer diagnosis. Modern Pathology, 38(5), 100715, 2025.
  8. Petäinen, L., Väyrynen, J.P., Böhm, J., Ruusuvuori, P., Ahtiainen, M., Elomaa, H., … Multi-scale ensemble model for dMMR prediction from histopathological images of colorectal cancer. 2025.

2024

  1. Paavolainen, O., Peurla, M., Koskinen, L.M., Pohjankukka, J., Saberi, K., … Volumetric analysis of the terminal ductal lobular unit architecture and cell phenotypes in the human breast. Cell Reports, 43(10), 2024.
  2. Tiihonen, A., Salonen, I., Koivisto, I., Ritamäki, A., Jaatinen, S., Hyvärinen, T., … P02.09.B Hypoxia modulates diffuse astrocytoma tumor microenvironment through cell-type-dependent responses. Neuro-Oncology, 26(Suppl 5), v36, 2024.
  3. Weitz, P., Valkonen, M., Solorzano, L., Carr, C., Kartasalo, K., Boissin, C., … Hartman, J., Ruusuvuori, P., Rantalainen, M. The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue. Medical Image Analysis, 97, 103257, 2024.
  4. Prezja, F., Annala, L., Kiiskinen, S., Lahtinen, S., Ojala, T., Ruusuvuori, P., … Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning. Heliyon, 10(18), 2024.
  5. Mikkola, L., Mikocziova, I., Piipponen, M., Valkonen, M., Fagersund, J., Palani, S., … Obesity-induced changes in perivascular adipose tissue fibroblasts in a mouse model of atherosclerosis. European Journal of Immunology, 54, 227–227, 2024.
  6. Latonen, L., Koivukoski, S., Khan, U., Ruusuvuori, P. Virtual staining for histology by deep learning. Trends in Biotechnology, 42(9), 1177–1191, 2024.
  7. Nurminen, V., Ravindran, A., Valkonen, M., Schmauch, E., Örd, T., Mikkola, L., … High-fidelity spatial transcriptomics reveals the distribution, interactions and relations of several inflammatory cell types in murine atherosclerotic plaques. Atherosclerosis, 395, 2024.
  8. Auvinen, A., Tammela, T.L.J., Mirtti, T., Lilja, H., Tolonen, T., Kenttämies, A., … Prostate cancer screening with PSA, kallikrein panel, and MRI: the ProScreen randomized trial. JAMA, 331(17), 1452–1459, 2024.
  9. Ruusuvuori, P., Hytönen, V. Artificial intelligence in clinical diagnostic pathology. 2024.
  10. Latonen, L., Khan, U., Koivukoski, S., Hartman, J., Ruusuvuori, P. Generative AI for virtual HE-staining of whole slide images of unstained breast cancer tissue. Cancer Research, 84(6_Supplement), 6185–6185, 2024.
  11. Batnasan, E., Kärkkäinen, M., Koivukoski, S., Ruusuvuori, P., Latonen, L. Phenotypic single-cell analysis of nuclear stress responses in drug-treated prostate cancer cells. Cancer Research, 84(6_Supplement), 4725–4725, 2024.
  12. Batnasan, E., Kärkkäinen, M., Koivukoski, S., Sadeesh, N., Tollis, S., Ruusuvuori, P., Scaravilli, M., Latonen, L. Platinum-based drugs induce phenotypic alterations in nucleoli and Cajal bodies in prostate cancer cells. Cancer Cell International, 24(1), 29, 2024.
  13. Koivukoski, S., Ruusuvuori, P., Latonen, L. Virtuaalivärjäys mahdollistaa ympäristöystävällisempää histologiaa kemikaaleja vähentämällä. Suomalainen lääkäriseura Duodecim, 2024.
  14. Mairinoja, L., Liimatainen, K., Koivukoski, S., Latonen, L., Strauss, L., … Virtuaalimaailma histologian oppimisen tukena. Yliopistopedagogiikka, 31(1), 2024.

2023

  1. Honkamaa, J., Khan, U., Koivukoski, S., Valkonen, M., Latonen, L., … Ruusuvuori, P., Marttinen, P. Deformation equivariant cross-modality image synthesis with paired non-aligned training data. Medical Image Analysis, 90, 102940, 2023.
  2. Rautajoki, K.J., Jaatinen, S., Hartewig, A., Tiihonen, A.M., Annala, M., Salonen, I., … Genomic characterization of IDH-mutant astrocytoma progression to grade 4 in the treatment setting. Acta Neuropathologica Communications, 11(1), 176, 2023.
  3. Prezja, F., Äyrämö, S., Pölönen, I., Ojala, T., Lahtinen, S., Ruusuvuori, P., … Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions. Scientific Reports, 13(1), 15879, 2023.
  4. Ruusuvuori, P., Valkonen, M., Latonen, L. Deep learning transforms colorectal cancer biomarker prediction from histopathology images. Cancer Cell, 41(9), 1543–1545, 2023.
  5. Rautajoki, K.J., Jaatinen, S., Hartewig, A., Tiihonen, A.M., … P10.14.B Progression of IDH-mutant astrocytomas to grade 4 under treatment-related selection pressure. Neuro-Oncology, 25(Supplement_2), ii65–ii65, 2023.
  6. Weitz, P., Valkonen, M., Solorzano, L., Carr, C., Kartasalo, K., Boissin, C., … A multi-stain breast cancer histological whole-slide-image data set from routine diagnostics. Scientific Data, 10(1), 562, 2023.
  7. Mairinoja, L., Heikelä, H., Blom, S., Kumar, D., Knuuttila, A., Boyd, S., Sjöblom, N., … Deep learning-based image analysis of liver steatosis in mouse models. The American Journal of Pathology, 193(8), 1072–1080, 2023.
  8. Petäinen, L., Väyrynen, J.P., Ruusuvuori, P., Pölönen, I., Äyrämö, S., Kuopio, T. Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer. PLoS ONE, 18(5), e0286270, 2023.
  9. Khan, U., Koivukoski, S., Valkonen, M., Latonen, L., Ruusuvuori, P. The effect of neural network architecture on virtual H&E staining: Systematic assessment of histological feasibility. Patterns, 4(5), 2023.
  10. Koivukoski, S., Khan, U., Ruusuvuori, P., Latonen, L. Unstained tissue imaging and virtual hematoxylin and eosin staining of histologic whole slide images. Laboratory Investigation, 103(5), 100070, 2023.
  11. Egevad, L., Delahunt, B., Iczkowski, K.A., van der Kwast, T., … Ruusuvuori, P., Eklund, M., Kartasalo, K. Interobserver reproducibility of cribriform cancer in prostate needle biopsies and validation of ISUP criteria. Histopathology, 82(6), 837–845, 2023.
  12. Ton, K., Wang, Y., Pan, L., Kartasalo, K., Acs, B., Weitz, P., … Validation of spatial gene expression patterns predicted by deep convolutional neural networks from breast cancer histopathology images. Cancer Research, 83(7_Supplement), 5432–5432, 2023.
  13. Meikar, O., Majoral, D., Heikkinen, O., Valkama, E., Leskinen, S., Rebane, A., Ruusuvuori, P., Toppari, J., Mäkelä, J-A., Kotaja, N. STAGETOOL, a novel automated approach for mouse testis histological analysis. Endocrinology, 164(2), bqac202, 2023.
  14. Liimatainen, K., Latonen, L., Ruusuvuori, P. 3D histology data of mouse prostates with manually annotated tumor hotspots. 2023.

2022

  1. Olsson, H., Kartasalo, K., Mulliqi, N., Capuccini, M., Ruusuvuori, P., … Eklund, M. Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction. Nature Communications, 13(1), 7761, 2022.
  2. Bulten, W., Kartasalo, K., Chen, P-H.C., Ström, P., Pinckaers, H., Nagpal, K., … Ruusuvuori, P.*, Litjens, G.*, Eklund, M.* Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nature Medicine, 28, 154–163, 2022. *Equal contribution
  3. Prezja, F., Pölönen, I., Äyrämö, S., Ruusuvuori, P., Kuopio, T. H&E multi-laboratory staining variance exploration with machine learning. Applied Sciences, 12(15), 7511, 2022.
  4. Mulliqi, N., Kartasalo, K., Olsson, H., Ji, X., Egevad, L., Eklund, M., Ruusuvuori, P. A Python application programming interface for accessing Philips iSyntax whole slide images for computational pathology. Medical Imaging with Deep Learning, 2022.
  5. Kartasalo, K., Ström, P., Ruusuvuori, P., Samaratunga, H., Delahunt, B., Tsuzuki, T., Eklund, M., Egevad, L. Detection of perineural invasion in prostate needle biopsies with deep neural networks. Virchows Archiv, 481, 73–82, 2022.
  6. Scaravilli, M., Koivukoski, S., Gillen, A., Bouazza, A., Ruusuvuori, P., Visakorpi, T., Latonen, L. miR-32 promotes MYC-driven prostate cancer. Oncogenesis, 11(1), 11, 2022.
  7. Ruusuvuori, P., Valkonen, M., Kartasalo, K., Visakorpi, T., Nykter, M., Latonen, L. Spatial analysis of histology in 3D: quantification and visualization of organ and tumor level tissue environment. Heliyon, 8(1), e08762, 2022.
  8. Ruusuvuori, P. Parametric modeling in biomedical image synthesis. Biomedical Image Synthesis and Simulation, 7–21, 2022.
  9. Weitz, P., Valkonen, M., Solorzano, L., Hartman, J., Ruusuvuori, P., Rantalainen, M. ACROBAT — automatic registration of breast cancer tissue. 10th International Workshop on Biomedical Image Registration, 2022.

2021

  1. Mulliqi, N., Kartasalo, K., Olsson, H., Ji, X., Egevad, L., Eklund, M., Ruusuvuori, P. OpenPhi: an interface to access Philips iSyntax whole slide images for computational pathology. Bioinformatics, 37(21), 3995–3997, 2021.
  2. Liimatainen, K., Latonen, L., Valkonen, M., Kartasalo, K., Ruusuvuori, P. Virtual reality for 3D histology: multi-scale visualization of organs with interactive feature exploration. BMC Cancer, 21(1), 1133, 2021.
  3. Wang, Y., Kartasalo, K., Weitz, P., Acs, B., Valkonen, M., Larsson, C., Ruusuvuori, P.*, Hartman, J.*, Rantalainen, M.* Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer. Cancer Research, 81(19), 5115–5126, 2021. *Equal contribution
  4. Kartasalo, K., Bulten, W., Delahunt, B., Chen, P-H.C., … Ruusuvuori, P., Egevad, L., Eklund, M. Artificial intelligence for Gleason grading of prostate biopsies — current status and next steps. European Urology Focus, 7(4), 687–691, 2021.
  5. Egevad, L., Delahunt, B., Samaratunga, H., Tsuzuki, T., Yamamoto, Y., Yaxley, J., Ruusuvuori, P., Kartasalo, K., Eklund, M. The emerging role of artificial intelligence in the reporting of prostate pathology. Pathology, 53(5), 565–567, 2021.
  6. Latonen, L., Ruusuvuori, P. Building a central repository landmarks a new era for AI-assisted digital pathology development in Europe. European Journal of Cancer, 150, 31–32, 2021.
  7. Egevad, L., Delahunt, B., Samaratunga, H., Tsuzuki, T., Olsson, H., Ström, P., … Ruusuvuori, P. Interobserver reproducibility of perineural invasion of prostatic adenocarcinoma in needle biopsies. Virchows Archiv, 478(6), 1109–1116, 2021.
  8. Liimatainen, K., Huttunen, R., Latonen, L., Ruusuvuori, P. Convolutional neural network-based artificial intelligence for classification of protein localization patterns. Biomolecules, 11(2), 264, 2021.
  9. Valkonen, M., Högnäs, G., Bova, G.S., Ruusuvuori, P. Generalized fixation invariant nuclei detection through domain adaptation based deep learning. IEEE Journal of Biomedical and Health Informatics, 25(5), 1747–1757, 2021.

2020

  1. Mehtonen, J., Teppo, S., Lahnalampi, M., Kokko, A., Kaukonen, R., Oksa, L., … Ruusuvuori, P., … Heinäniemi, M. Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1-positive pediatric leukemia. Genome Medicine, 12(1), 99, 2020.
  2. Borovec, J., Kybic, J., Arganda-Carreras, I., … Ruusuvuori, P., … Muñoz-Barrutia, A. ANHIR: Automatic non-rigid histological image registration challenge. IEEE Transactions on Medical Imaging, 39(10), 3042–3052, 2020.
  3. Doan, P., Musa, A., Murugesan, A., Sipilä, V., Candeias, N.R., Emmert-Streib, F., Ruusuvuori, P., Granberg, K., Yli-Harja, O., Kandhavelu, M. Glioblastoma multiforme stem cell cycle arrest by alkylaminophenol through the modulation of EGFR and CSC signaling pathways. Cells, 9(3), 681, 2020.
  4. Ström, P., Kartasalo, K., Olsson, H., Solorzano, L., Delahunt, B., Berney, D.M., … Ruusuvuori, P., … Eklund, M. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. The Lancet Oncology, 21(2), 222–232, 2020.
  5. Valkonen, M., Isola, J., Ylinen, O., Muhonen, V., Saxlin, A., Tolonen, T., Nykter, M., Ruusuvuori, P. Cytokeratin-supervised deep learning for automatic recognition of epithelial cells in breast cancers stained for ER, PR, and Ki-67. IEEE Transactions on Medical Imaging, 39(2), 534–542, 2020.

2019

  1. Eerola, S., Santio, N., Rinne, S., Kouvonen, P., Corthals, G., Scaravilli, M., … Ruusuvuori, P., Latonen, L., Visakorpi, T., Koskinen, P. Phosphorylation of NFATC1 at PIM1 target sites is essential for its ability to promote prostate cancer cell migration and invasion. Cell Communication and Signaling, 17(1), 148, 2019.
  2. Liimatainen, K., Kananen, L., Latonen, L., Ruusuvuori, P. Iterative unsupervised domain adaptation for generalized cell detection from brightfield z-stacks. BMC Bioinformatics, 20(1), 80, 2019.
  3. Liimatainen, K., Kartasalo, K., Latonen, L., Ruusuvuori, P. 3D-printed whole prostate models with tumor hotspots using dual-extruder printer. Proc. IEEE EMBC 2019, Berlin, Germany, 2019.

2017–2018

  1. Kartasalo, K., Latonen, L., Vihinen, M., Visakorpi, T., Nykter, M., Ruusuvuori, P. Comparative analysis of tissue reconstruction algorithms for 3D histology. Bioinformatics, 34(17), 3013–3021, 2018.
  2. Liimatainen, K., Valkonen, M., Latonen, L., Ruusuvuori, P. Cell organelle classification with fully convolutional neural networks. 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), 2018.
  3. Högnäs, G., Kivinummi, K., Kallio, H.M.L., Hieta, R., Ruusuvuori, P., … Bova, G.S. Feasibility of prostate PAXgene fixation for molecular research and diagnostic surgical pathology. The American Journal of Surgical Pathology, 42(1), 103–115, 2018.
  4. Ehteshami Bejnordi, B., Veta, M., van Diest, P.J., … The CAMELYON16 Consortium. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 318(22), 2199–2210, 2017.
  5. Latonen, L., Scaravilli, M., Gillen, A., Zhang, F-P., Ruusuvuori, P., Kujala, P., Poutanen, M., Visakorpi, T. In vivo expression of miR-32 induces proliferation in prostate epithelium. The American Journal of Pathology, 187(11), 2546–2557, 2017.
  6. Valkonen, M., Kartasalo, K., Liimatainen, K., Nykter, M., Latonen, L., Ruusuvuori, P. Metastasis detection from whole slide images using local features and random forests. Cytometry Part A, 91(6), 555–565, 2017.
  7. Valkonen, M.*, Ruusuvuori, P.*, Kartasalo, K., Nykter, M., Visakorpi, T., Latonen, L. Analysis of spatial heterogeneity in normal epithelium and preneoplastic alterations in mouse prostate tumor models. Scientific Reports, 7(1), 44831, 2017. *Joint first authors
  8. Valkonen, M., Kartasalo, K., Liimatainen, K., Nykter, M., Latonen, L., Ruusuvuori, P. Dual structured convolutional neural network with feature augmentation for quantitative characterization of tissue histology. Proc. ICCV, pp. 27–35, 2017.
  9. Granberg, K.J., Annala, M., Lehtinen, B., Kesseli, J., Haapasalo, J., Ruusuvuori, P., … Zhang, W. Strong FGFR3 staining is a marker for FGFR3 fusions in diffuse gliomas. Neuro-oncology, 19(9), 1206–1216, 2017.
  10. Huttunen, M., Turkki, P., Mäki, A., Paavolainen, L., Ruusuvuori, P., Marjomäki, V. Echovirus 1 internalization negatively regulates EGFR down-regulation. Cellular Microbiology, 19(3), e12671, 2017.

2015–2016

  1. Ulman, V., Svoboda, D., Nykter, M., Kozubek, M., Ruusuvuori, P. Virtual cell imaging: A review on simulation methods employed in image cytometry. Cytometry Part A, 89(12), 1057–1072, 2016.
  2. Ruusuvuori, P., Valkonen, M., Nykter, M., Visakorpi, T., Latonen, L. Feature-based analysis of mouse prostatic intraepithelial neoplasia in histological tissue sections. Journal of Pathology Informatics, 7(1), 5, 2016.
  3. Kumpumäki, T., Ruusuvuori, P., Kangasniemi, V., Lipping, T. Data-driven approach to benthic cover type classification using bathymetric LiDAR waveform analysis. Remote Sensing, 7(10), 13390–13409, 2015.
  4. Annala, M., Kivinummi, K., Tuominen, J., … Latonen, L., … Recurrent SKIL-activating rearrangements in ETS-negative prostate cancer. Oncotarget, 6(8), 6235, 2015.
  5. Liimatainen, K., Heikkilä, R., Yli-Harja, O., Huttunen, H., Ruusuvuori, P. Sparse logistic regression and polynomial modelling for detection of artificial drainage networks. Remote Sensing Letters, 6(4), 311–320, 2015.
  6. Hassan, S.S., Ruusuvuori, P., Latonen, L., Huttunen, H. Flow cytometry-based classification in cancer research: A view on feature selection. Cancer Informatics, 14, CIN.S30795, 2015.

2010–2014

  1. Garmendia-Torres, C., Skupin, A., Michael, S.A., Ruusuvuori, P., Kuwada, N.J., … Unidirectional P-body transport during the yeast cell cycle. PLoS ONE, 9(6), e99428, 2014.
  2. Ruusuvuori, P., Paavolainen, L., Rutanen, K., Mäki, A., Huttunen, H., … Quantitative analysis of dynamic association in live biological fluorescent samples. PLoS ONE, 9(4), e94245, 2014.
  3. Ruusuvuori, P., Lin, J., Scott, A.C., Tan, Z., Sorsa, S., Kallio, A., Nykter, M., … Quantitative analysis of colony morphology in yeast. BioTechniques, 56(1), 18–27, 2014.
  4. Manninen, T., Huttunen, H., Ruusuvuori, P., Nykter, M. Leukemia prediction using sparse logistic regression. PLoS ONE, 8(8), e72932, 2013.
  5. Aghaeepour, N., Finak, G., FlowCAP Consortium, Dream Consortium, … Critical assessment of automated flow cytometry data analysis techniques. Nature Methods, 10(3), 228–238, 2013.
  6. Farhan, M., Ruusuvuori, P., Emmenlauer, M., Rämö, P., Dehio, C., Yli-Harja, O. Multi-scale Gaussian representation and outline-learning based cell image segmentation. BMC Bioinformatics, 14(Suppl 10), S6, 2013.
  7. Sarkanen, J.R., Ruusuvuori, P., Kuokkanen, H., Paavonen, T., Ylikomi, T. Bioactive acellular implant induces angiogenesis and adipogenesis and sustained soft tissue restoration in vivo. Tissue Engineering Part A, 18(23–24), 2568–2580, 2012.
  8. Oinonen, H., Forsvik, H., Ruusuvuori, P., Yli-Harja, O., Voipio, V., Huttunen, H. Identity verification based on vessel matching from fundus images. IEEE International Conference on Image Processing, 4089–4092, 2010.
  9. Erkkilä, T., Lehmusvaara, S., Ruusuvuori, P., Visakorpi, T., Shmulevich, I., Lähdesmäki, H. Probabilistic analysis of gene expression measurements from heterogeneous tissues. Bioinformatics, 26(20), 2571–2577, 2010.
  10. Aho, T., Almusa, H., Matilainen, J., Larjo, A., Ruusuvuori, P., … Reconstruction and validation of RefRec: a global model for the yeast molecular interaction network. PLoS ONE, 5(5), e10662, 2010.
  11. Ruusuvuori, P., Äijö, T., Chowdhury, S., Garmendia-Torres, C., Selinummi, J., … Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images. BMC Bioinformatics, 11(1), 248, 2010.
  12. Manninen, T., Pekkanen, V., Rutanen, K., Ruusuvuori, P., Huttunen, H., … Alignment of individually adapted print patterns for ink jet printed electronics. Journal of Imaging Science and Technology, 54(5), 50306-1–50306-15, 2010.

2006–2009

  1. Selinummi, J., Ruusuvuori, P., Podolsky, I., Ozinsky, A., Gold, E., Yli-Harja, O., … Shmulevich, I. Bright field microscopy as an alternative to whole cell fluorescence in automated analysis of macrophage images. PLoS ONE, 4(10), e7497, 2009.
  2. Ruusuvuori, P., Seppälä, J., Erkkilä, T., Lehmussola, A., Puhakka, J.A., … Efficient automated method for image-based classification of microbial cells. Proc. International Conference on Pattern Recognition, 1–4, 2008.
  3. Ruusuvuori, P., Lehmussola, A., Selinummi, J., Rajala, T., Huttunen, H., … Benchmark set of synthetic images for validating cell image analysis algorithms. Proc. European Signal Processing Conference, 1–5, 2008.
  4. Lehmussola, A., Ruusuvuori, P., Selinummi, J., Rajala, T., Huttunen, H., Yli-Harja, O. Synthetic images of high-throughput microscopy for validation of image analysis methods. Proceedings of the IEEE, 96(8), 1348–1360, 2008.
  5. Niemistö, A., Nykter, M., Aho, T., Jalovaara, H., Marjanen, K., Ahdesmäki, M., … Computational methods for estimation of cell cycle phase distributions of yeast cells. EURASIP Journal on Bioinformatics and Systems Biology, 2007(1), 46150, 2007.
  6. Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O. Computational framework for simulating fluorescence microscope images with cell populations. IEEE Transactions on Medical Imaging, 26(7), 1010–1016, 2007.
  7. Lehmussola, A., Ruusuvuori, P., Yli-Harja, O. Evaluating the performance of microarray segmentation algorithms. Bioinformatics, 22(23), 2910–2917, 2006.
  8. Nykter, M., Aho, T., Ahdesmäki, M., Ruusuvuori, P., Lehmussola, A., Yli-Harja, O. Simulation of microarray data with realistic characteristics. BMC Bioinformatics, 7(1), 349, 2006.

Bioimage Informatics Group • Institute of Biomedicine • University of Turku, Finland