Publications

[1]         J. Chakraborty et al., “Use of Response Permutation to Measure an Imaging Dataset’s Susceptibility to Overfitting by Selected Standard Analysis Pipelines,” Acad. Radiol., Apr. 2024, doi: 10.1016/j.acra.2024.02.028.

[2]         E. C. Nakajima et al., “Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group,” JCO Precis. Oncol., no. 8, p. e2300687, Apr. 2024, doi: 10.1200/PO.23.00687.

[3]         B. J. Laight et al., “Fes-deficient macrophages enhance CD8 ⁺ T cell priming and tumour control through increased proinflammatory cytokine production and presentation.” Mar. 02, 2024. doi: 10.1101/2024.02.27.581601.

[4]         M. Hamghalam and A. L. Simpson, “Medical image synthesis via conditional GANs: Application to segmenting brain tumours,” Comput. Biol. Med., vol. 170, p. 107982, Mar. 2024, doi: 10.1016/j.compbiomed.2024.107982.

[5]         A. L. Simpson et al., “Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases,” Sci. Data, vol. 11, no. 1, p. 172, Feb. 2024, doi: 10.1038/s41597-024-02981-2.

[6]         M. Hamghalam et al., “Machine Learning Detection and Characterization of Splenic Injuries on Abdominal Computed Tomography,” Can. Assoc. Radiol. J., p. 08465371231221052, Jan. 2024, doi: 10.1177/08465371231221052.

[7]         A. Robins et al., “The Association between COVID-19 and Changes in Opioid Prescribing Patterns and Opioid-Related Overdoses: A Retrospective Cohort Study,” Can. J. Pain, vol. 7, no. 1, p. 2176297, Dec. 2023, doi: 10.1080/24740527.2023.2176297.

[8]         M. Hamghalam, R. K. G. Do, and A. L. Simpson, “Attention-based CT scan interpolation for lesion segmentation of colorectal liver metastases,” in Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging, SPIE, Apr. 2023, pp. 186–193. doi: 10.1117/12.2656072.

[9]         A. Midya et al., “Computerized Diagnosis of Liver Tumors From CT Scans Using a Deep Neural Network Approach,” IEEE J. Biomed. Health Inform., vol. 27, no. 5, pp. 2456–2464, May 2023, doi: 10.1109/JBHI.2023.3248489.

[10]       “Med-ImageTools: An open-source Python package for… | F1000Research.” Accessed: Jul. 12, 2023. [Online]. Available: https://f1000research.com/articles/12-118

[11]       S. Pati et al., “Author Correction: Federated learning enables big data for rare cancer boundary detection,” Nat. Commun., vol. 14, no. 1, Art. no. 1, Jan. 2023, doi: 10.1038/s41467-023-36188-7.

[12]       S. Pati et al., “Federated learning enables big data for rare cancer boundary detection,” Nat. Commun., vol. 13, no. 1, Art. no. 1, Dec. 2022, doi: 10.1038/s41467-022-33407-5.

[13]       R. Hu et al., “Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy,” Phys. Imaging Radiat. Oncol., vol. 24, pp. 36–42, Oct. 2022, doi: 10.1016/j.phro.2022.09.004.

[14]       R. Mojtahedi, M. Hamghalam, R. K. G. Do, and A. L. Simpson, “Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation,” in Multiscale Multimodal Medical Imaging, X. Li, J. Lv, Y. Huo, B. Dong, R. M. Leahy, and Q. Li, Eds., in Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2022, pp. 110–120. doi: 10.1007/978-3-031-18814-5_11.

[15]       P. Causa Andrieu et al., “Natural Language Processing of Computed Tomography Reports to Label Metastatic Phenotypes With Prognostic Significance in Patients With Colorectal Cancer,” JCO Clin. Cancer Inform., no. 6, p. e2200014, Dec. 2022, doi: 10.1200/CCI.22.00014.

[16]       N. Horvat et al., “A primer on texture analysis in abdominal radiology,” Abdom. Radiol., vol. 47, no. 9, pp. 2972–2985, Sep. 2022, doi: 10.1007/s00261-021-03359-3.

[17]       S. P. Phillips, S. Spithoff, and A. Simpson, “Artificial intelligence and predictive algorithms in medicine: Promise and problems,” Can. Fam. Physician, vol. 68, no. 8, pp. 570–572, Aug. 2022, doi: 10.46747/cfp.6808570.

[18]       C. S. Moskowitz, M. L. Welch, M. A. Jacobs, B. F. Kurland, and A. L. Simpson, “Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias                     Mitigation Strategies,” Radiology, vol. 304, no. 2, pp. 265–273, Aug. 2022, doi: 10.1148/radiol.211597.

[19]       C. P. Zambirinis et al., “ASO Visual Abstract: Recurrence After Resection of Pancreatic Cancer – Can Radiomics Predict Patients at Greatest Risk of Liver Metastasis?,” Ann. Surg. Oncol., vol. 29, no. 8, pp. 4977–4978, Aug. 2022, doi: 10.1245/s10434-022-11674-2.

[20]       C. P. Zambirinis et al., “Recurrence After Resection of Pancreatic Cancer: Can Radiomics Predict Patients at Greatest Risk of Liver Metastasis?,” Ann. Surg. Oncol., vol. 29, no. 8, pp. 4962–4974, Aug. 2022, doi: 10.1245/s10434-022-11579-0.

[21]       M. Antonelli et al., “The Medical Segmentation Decathlon,” Nat. Commun., vol. 13, no. 1, p. 4128, Jul. 2022, doi: 10.1038/s41467-022-30695-9.

[22]       A. Kearney et al., “Prediction of hospitalization and icu admission for ontario covid-19 patients with cardiac comorbidities,” J. Am. Coll. Cardiol., vol. 79, no. 9_Supplement, pp. 1843–1843, Mar. 2022, doi: 10.1016/S0735-1097(22)02834-0.

[23]       K. E. Batch et al., “Developing a Cancer Digital Twin: Supervised Metastases Detection From Consecutive Structured Radiology Reports,” Front. Artif. Intell., vol. 5, p. 826402, Mar. 2022, doi: 10.3389/frai.2022.826402.

[24]       S. Sun et al., “Natural Language Processing of Large-Scale Structured Radiology Reports to Identify Oncologic Patients With or Without Splenomegaly Over a 10-Year Period,” JCO Clin. Cancer Inform., no. 6, p. e2100104, Dec. 2022, doi: 10.1200/CCI.21.00104.

[25]       R. K. G. Do et al., “Patterns of Metastatic Disease in Patients with Cancer Derived from Natural Language Processing of Structured CT Radiology Reports over a 10-year Period,” Radiology, vol. 301, no. 1, pp. 115–122, Oct. 2021, doi: 10.1148/radiol.2021210043.

[26]       T. Boerner et al., “Genetic Determinants of Outcome in Intrahepatic Cholangiocarcinoma,” Hepatology, vol. 74, no. 3, pp. 1429–1444, 2021, doi: 10.1002/hep.31829.

[27]       S. Crête et al., “PD-0735 Time-dependent machine learning survival prediction model of brain metastases with MRI radiomics,” Radiother. Oncol., vol. 161, pp. S565–S566, 2021.

[28]       S. Thirumal et al., “Utility of High-Throughput Imaging Mass Cytometry for Cancer Research: A feasibility study,” in 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Jul. 2021, pp. 1–4. doi: 10.1109/BHI50953.2021.9508569.

[29]       A. Pulvirenti et al., “Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade,” JCO Clin. Cancer Inform., no. 5, pp. 679–694, Dec. 2021, doi: 10.1200/CCI.20.00121.

[30]       S. M. Dickinson et al., “Preoperative CT predictors of survival in patients with pancreatic ductal adenocarcinoma undergoing curative intent surgery,” Abdom. Radiol., vol. 46, no. 4, pp. 1607–1617, Apr. 2021, doi: 10.1007/s00261-020-02726-w.

[31]       J. M. Creasy et al., “Differences in Liver Parenchyma are Measurable with CT Radiomics at Initial Colon Resection in Patients that Develop Hepatic Metastases from Stage II/III Colon Cancer,” Ann. Surg. Oncol., vol. 28, no. 4, pp. 1982–1989, Apr. 2021, doi: 10.1245/s10434-020-09134-w.

[32]       H. Muhammad et al., “EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, with Prognostic Stratification Boosting,” in Medical Imaging with Deep Learning, 2021. [Online]. Available: https://openreview.net/forum?id=JSSwHS_GU63

[33]       T. L. Williams, L. V. Saadat, M. Gonen, A. Wei, R. K. G. Do, and A. L. Simpson, “Radiomics in surgical oncology: applications and challenges,” Comput. Assist. Surg., vol. 26, no. 1, pp. 85–96, Jan. 2021, doi: 10.1080/24699322.2021.1994014.

[34]       D. Bounias et al., “Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and Organs,” Appl. Sci., vol. 11, no. 16, Art. no. 16, Jan. 2021, doi: 10.3390/app11167488.

[35]       M. Hamghalam, A. F. Frangi, B. Lei, and A. L. Simpson, “Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-modal Glioma Segmentation,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, M. de Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, and C. Essert, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2021, pp. 442–452. doi: 10.1007/978-3-030-87234-2_42.

[36]       A. Calò et al., “Spatial mapping of the collagen distribution in human and mouse tissues by force volume atomic force microscopy,” Sci. Rep., vol. 10, no. 1, Art. no. 1, Sep. 2020, doi: 10.1038/s41598-020-72564-9.

[37]       W.-C. Liao, A. L. Simpson, and W. Wang, “Convolutional neural network for the detection of pancreatic cancer on CT scans – Authors’ reply,” Lancet Digit. Health, vol. 2, no. 9, p. e454, Sep. 2020, doi: 10.1016/S2589-7500(20)30188-6.

[38]       A. Hoshino et al., “Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers,” Cell, vol. 182, no. 4, pp. 1044-1061.e18, Aug. 2020, doi: 10.1016/j.cell.2020.07.009.

[39]       A. Simpson and M. Miga, “Special Section Guest Editorial: Interventional and Surgical Data Science for Data-Driven Patient Outcomes,” J. Med. Imaging, vol. 7, no. 3, p. 031501, May 2020, doi: 10.1117/1.JMI.7.3.031501.

[40]       S. Narasimhan, J. A. Weis, M. Luo, A. L. Simpson, R. C. Thompson, and M. I. Miga, “Accounting for intraoperative brain shift ascribable to cavity collapse during intracranial tumor resection,” J. Med. Imaging, vol. 7, no. 3, p. 031506, Jun. 2020, doi: 10.1117/1.JMI.7.3.031506.

[41]       K. A. Harrington et al., “Multimodal radiomics and cyst fluid inflammatory markers model to predict preoperative risk in intraductal papillary mucinous neoplasms,” J. Med. Imaging, vol. 7, no. 3, p. 031507, Jun. 2020, doi: 10.1117/1.JMI.7.3.031507.

[42]       T. L. Williams et al., “A combined radiomics and cyst fluid inflammatory markers model to predict preoperative risk in pancreatic cystic lesions,” in Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2020, pp. 446–451. doi: 10.1117/12.2566425.

[43]       A. L. SIMPSON and R. K. G. DO, “System, method and computer-accessible medium for texture analysis of hepatopancreatobiliary diseases,” US10552969B2, Feb. 04, 2020 Accessed: Jul. 19, 2023. [Online]. Available: https://patents.google.com/patent/US10552969B2/en

[44]       R. Yamashita et al., “Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation,” Eur. Radiol., vol. 30, no. 1, pp. 195–205, Jan. 2020, doi: 10.1007/s00330-019-06381-8.

[45]       J. Gagniere et al., “Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation,” Abdom. Radiol. Online, vol. 44, no. 11, Nov. 2019, doi: 10.1007/S00261-019-02117-W.

[46]       M. A. Attiyeh et al., “CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma,” Abdom. Radiol., vol. 44, no. 9, pp. 3148–3157, Sep. 2019, doi: 10.1007/s00261-019-02112-1.

[47]       T. Wang et al., “Distinct histomorphological features are associated with IDH1 mutation in intrahepatic cholangiocarcinoma,” Hum. Pathol., vol. 91, pp. 19–25, Sep. 2019, doi: 10.1016/j.humpath.2019.05.002.

[48]       C. A. McIntyre et al., “Abstract 2444: The use of CT radiomics to predict immune infiltrate in pancreatic ductal adenocarcinoma,” Cancer Res., vol. 79, no. 13_Supplement, p. 2444, Jul. 2019, doi: 10.1158/1538-7445.AM2019-2444.

[49]       L. M. Pak et al., “Utility of Image Guidance in the Localization of Disappearing Colorectal Liver Metastases,” J. Gastrointest. Surg., vol. 23, no. 4, pp. 760–767, Apr. 2019, doi: 10.1007/s11605-019-04106-2.

[50]       H. Muhammad et al., “Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual Dictionary.” arXiv, Mar. 12, 2019. doi: 10.48550/arXiv.1903.05257.

[51]       A. L. Simpson et al., “A large annotated medical image dataset for the development and evaluation of segmentation algorithms.” arXiv, Feb. 24, 2019. doi: 10.48550/arXiv.1902.09063.

[52]       R. R. Narayan et al., “Regional differences in gallbladder cancer pathogenesis: Insights from a multi-institutional comparison of tumor mutations,” Cancer, vol. 125, no. 4, pp. 575–585, 2019, doi: 10.1002/cncr.31850.

[53]       M. A. Attiyeh et al., “Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis,” HPB, vol. 21, no. 2, pp. 212–218, Feb. 2019, doi: 10.1016/j.hpb.2018.07.016.

[54]       H. Muhammad et al., “Unsupervised Subtyping of Cholangiocarcinoma Using a Deep Clustering Convolutional Autoencoder,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, D. Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P.-T. Yap, and A. Khan, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019, pp. 604–612. doi: 10.1007/978-3-030-32239-7_67.

[55]       J. M. Creasy et al., “Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases,” Eur. Radiol., vol. 29, no. 1, pp. 458–467, Jan. 2019, doi: 10.1007/s00330-018-5542-8.

[56]       T. Perrin et al., “Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging,” Abdom. Radiol., vol. 43, no. 12, pp. 3271–3278, Dec. 2018, doi: 10.1007/s00261-018-1600-6.

[57]       J. Chakraborty et al., “CT radiomics to predict high‐risk intraductal papillary mucinous neoplasms of the pancreas,” Med. Phys., vol. 45, no. 11, pp. 5019–5029, Nov. 2018, doi: 10.1002/mp.13159.

[58]       A. D. Speers, B. Ma, W. R. Jarnagin, S. Himidan, A. L. Simpson, and R. P. Wildes, “Fast and accurate vision-based stereo reconstruction and motion estimation for image-guided liver surgery,” Healthc. Technol. Lett., vol. 5, no. 5, pp. 208–214, 2018, doi: 10.1049/htl.2018.5071.

[59]       C. S. Sigel et al., “INTRAHEPATIC CHOLANGIOCARCINOMAS HAVE HISTOLOGICALLY AND IMMUNOPHENOTYPICALLY DISTINCT SMALL AND LARGE DUCT PATTERNS,” Am. J. Surg. Pathol., vol. 42, no. 10, pp. 1334–1345, Oct. 2018, doi: 10.1097/PAS.0000000000001118.

[60]       E. A. Aherne et al., “Intrahepatic cholangiocarcinoma: can imaging phenotypes predict survival and tumor genetics?,” Abdom. Radiol., vol. 43, no. 10, pp. 2665–2672, Oct. 2018, doi: 10.1007/s00261-018-1505-4.

[61]       L. M. Pak et al., “Can physician gestalt predict survival in patients with resectable pancreatic adenocarcinoma?,” Abdom. Radiol., vol. 43, no. 8, pp. 2113–2118, Aug. 2018, doi: 10.1007/s00261-017-1407-x.

[62]       L. M. Pak et al., “Quantitative Imaging Features and Postoperative Hepatic Insufficiency: A Multi-Institutional Expanded Cohort,” J. Am. Coll. Surg., vol. 226, no. 5, pp. 835–843, May 2018, doi: 10.1016/j.jamcollsurg.2018.02.001.

[63]       A. Simpson and M. Miga, “Special Section Guest Editorial: Technology Platforms for Treatment and Discovery in Human Systems: Novel Work in Image-Guided Procedures, Robotic Interventions, and Modeling,” J. Med. Imaging, vol. 5, no. 02, p. 1, May 2018, doi: 10.1117/1.JMI.5.2.021201.

[64]       M. A. Attiyeh et al., “Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis,” Ann. Surg. Oncol., vol. 25, no. 4, pp. 1034–1042, Apr. 2018, doi: 10.1245/s10434-017-6323-3.

[65]       J. S. Heiselman et al., “Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery,” J. Med. Imaging, vol. 5, no. 2, p. 021203, Dec. 2017, doi: 10.1117/1.JMI.5.2.021203.

[66]       J. S. Heiselman et al., “Technical note: nonrigid registration for laparoscopic liver surgery using sparse intraoperative data,” in Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2018, pp. 73–79. doi: 10.1117/12.2295026.

[67]       C. P. Zambirinis et al., “Patterns of recurrence and peri-operative predictors of liver metastasis after pancreatic cancer resection,” HPB, vol. 20, p. S145, Mar. 2018, doi: 10.1016/j.hpb.2018.02.565.

[68]       T. Wang et al., “Histoarchitectural pattern does not distinguish IDH1 mutant intrahepatic cholangiocarcinomas from non-IDH mutant controls,” in LABORATORY INVESTIGATION, NATURE PUBLISHING GROUP 75 VARICK ST, 9TH FLR, NEW YORK, NY 10013-1917 USA, 2018, pp. 651–651.

[69]       T. P. Kingham et al., “3D image guidance assisted identification of colorectal cancer liver metastases not seen on intraoperative ultrasound: results from a prospective trial,” HPB, vol. 20, no. 3, pp. 260–267, Mar. 2018, doi: 10.1016/j.hpb.2017.08.035.

[70]       A. Midya et al., “Deep convolutional neural network for the classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma,” in Medical Imaging 2018: Computer-Aided Diagnosis, SPIE, Feb. 2018, pp. 501–506. doi: 10.1117/12.2293683.

[71]       J. Chakraborty et al., “Quantitative CT analysis for the preoperative prediction of pathologic grade in pancreatic neuroendocrine tumors,” in Medical Imaging 2018: Computer-Aided Diagnosis, SPIE, 2018, pp. 381–386.

[72]       S. Lawrence et al., “Use of Quantitative Image Analysis and Cyst Fluid Inflammatory Markers to Predict Risk in Intraductal Papillary Mucinous Neoplasms,” in ANNALS OF SURGICAL ONCOLOGY, SPRINGER 233 SPRING ST, NEW YORK, NY 10013 USA, 2018, pp. S131–S131.

[73]       S. Aylward, A. Simpson, D. Stoyanov, J. M. R. Tavares, Z. Taylor, and Y. Xiao, Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation: International Workshops, POCUS 2018, BIVPCS 2018, CuRIOUS 2018, and CPM 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings. Springer, 2018.

[74]       S. Bakas et al., “CPM 2018 preface: International Workshop on Point-of-Care Ultrasound, POCUS 2018, the International Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2017, the International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2018, and the International Workshop on Computational Precision Medicine, CPM 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018,” Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma., vol. 11042 LNCS, p. XI, 2018.

[75]       A. Midya, J. Chakraborty, M. Gönen, R. K. G. D. M.d, and A. L. Simpson, “Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility,” J. Med. Imaging, vol. 5, no. 1, p. 011020, Feb. 2018, doi: 10.1117/1.JMI.5.1.011020.

[76]       N. Horvat et al., “Imaging features of hepatocellular carcinoma compared to intrahepatic cholangiocarcinoma and combined tumor on MRI using liver imaging and data system (LI-RADS) version 2014,” Abdom. Radiol., vol. 43, no. 1, pp. 169–178, Jan. 2018, doi: 10.1007/s00261-017-1261-x.

[77]       J. Chakraborty et al., “Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients,” PLOS ONE, vol. 12, no. 12, p. e0188022, Dec. 2017, doi: 10.1371/journal.pone.0188022.

[78]       J. Zheng et al., “Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma Using Quantitative Image Analysis,” J. Am. Coll. Surg., vol. 225, no. 6, pp. 778-788.e1, Dec. 2017, doi: 10.1016/j.jamcollsurg.2017.09.003.

[79]       L. M. Pak et al., “Quantitative Imaging Features of Preoperative Computed Tomography Images Predict Post-Hepatectomy Liver Insufficiency: A Multi-Institutional Expansion Cohort,” J. Am. Coll. Surg., vol. 225, no. 4, Supplement 1, p. S137, Oct. 2017, doi: 10.1016/j.jamcollsurg.2017.07.306.

[80]       L. W. Clements et al., “Deformation correction for image guided liver surgery: An intraoperative fidelity assessment,” Surgery, vol. 162, no. 3, pp. 537–547, Sep. 2017, doi: 10.1016/j.surg.2017.04.020.

[81]       A. L. Simpson et al., “Computed Tomography Image Texture: A Noninvasive Prognostic Marker of Hepatic Recurrence After Hepatectomy for Metastatic Colorectal Cancer,” Ann. Surg. Oncol., vol. 24, no. 9, pp. 2482–2490, Sep. 2017, doi: 10.1245/s10434-017-5896-1.

[82]       B. Ma, T. P. Kingham, M. I. Miga, W. R. Jarnagin, and A. L. Simpson, “Liver segmentation in color images,” in Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Aug. 2017, pp. 436–441. doi: 10.1117/12.2255393.

[83]       J. A. Collins et al., “Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework,” IEEE Trans. Med. Imaging, vol. 36, no. 7, pp. 1502–1510, Jul. 2017, doi: 10.1109/TMI.2017.2668842.

[84]       L. M. Pak et al., “Mutational profiling of resected intrahepatic cholangiocarcinoma.,” J. Clin. Oncol., vol. 35, no. 15_suppl, pp. e15675–e15675, May 2017, doi: 10.1200/JCO.2017.35.15_suppl.e15675.

[85]       W. R. Jarnagin, A. L. Simpson, and M. I. Miga, “Toward integrated image guided liver surgery,” in Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Apr. 2017, pp. 223–228. doi: 10.1117/12.2257615.

[86]       M. A. Attiyeh et al., “Behind the cyst: predicting grade of dysplasia in intraductal papillary mucinous neoplasms (IPMNs) by quantitative image analysis,” HPB, vol. 19, p. S22, Apr. 2017, doi: 10.1016/j.hpb.2017.02.201.

[87]       B. Ma, N. Banihaveb, J. Choi, E. C. S. Chen, and A. L. Simpson, “Is pose-based pivot calibration superior to sphere fitting?,” in Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2017, pp. 476–483. doi: 10.1117/12.2256050.

[88]       J. Chakraborty et al., “Preoperative assessment of microvascular invasion in hepatocellular carcinoma,” in Medical Imaging 2017: Computer-Aided Diagnosis, SPIE, Mar. 2017, pp. 248–254. doi: 10.1117/12.2255622.

[89]       L. Gazit et al., “Quantification of CT images for the classification of high- and low-risk pancreatic cysts,” in Medical Imaging 2017: Computer-Aided Diagnosis, SPIE, Mar. 2017, pp. 220–225. doi: 10.1117/12.2255626.

[90]       J. A. Collins et al., “On the nature of data collection for soft-tissue image-to-physical organ registration: a noise characterization study,” in Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2017, pp. 495–506. doi: 10.1117/12.2255844.

[91]       J. S. Heiselman et al., “Emulation of the laparoscopic environment for image-guided liver surgery via an abdominal phantom system with anatomical ligamenture,” in Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2017, pp. 744–752. doi: 10.1117/12.2255842.

[92]       M. Attiyeh et al., “Through the Looking-Mass: Preoperative Survival Prediction in Pancreatic Ductal Adenocarcinoma (PDAC) by Quantitative CT Analysis,” in ANNALS OF SURGICAL ONCOLOGY, SPRINGER 233 SPRING ST, NEW YORK, NY 10013 USA, 2017, pp. S99–S99.

[93]       R. Ong et al., “A novel method for texture-mapping conoscopic surfaces for minimally invasive image-guided kidney surgery,” Int. J. Comput. Assist. Radiol. Surg., vol. 11, no. 8, pp. 1515–1526, Aug. 2016, doi: 10.1007/s11548-015-1339-2.

[94]       M. I. Miga et al., “Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases,” Int. J. Comput. Assist. Radiol. Surg., vol. 11, no. 8, pp. 1467–1474, Aug. 2016, doi: 10.1007/s11548-015-1295-x.

[95]       A. L. Simpson and T. P. Kingham, “Current Evidence in Image-Guided Liver Surgery,” J. Gastrointest. Surg., vol. 20, no. 6, pp. 1265–1269, Jun. 2016, doi: 10.1007/s11605-016-3101-7.

[96]       J. Chakraborty et al., “Texture analysis for survival prediction of pancreatic ductal adenocarcinoma patients with neoadjuvant chemotherapy,” in Medical Imaging 2016: Image Processing, SPIE, Mar. 2016, pp. 505–510. doi: 10.1117/12.2214470.

[97]       L. W. Clements et al., “Evaluation of model-based deformation correction in image-guided liver surgery via tracked intraoperative ultrasound,” J. Med. Imaging, vol. 3, no. 1, p. 015003, Mar. 2016, doi: 10.1117/1.JMI.3.1.015003.

[98]       G. S. Herbert et al., “Early trends in serum phosphate and creatinine levels are associated with mortality following major hepatectomy,” HPB, vol. 17, no. 12, pp. 1058–1065, Dec. 2015, doi: 10.1111/hpb.12483.

[99]       B. J. Joiner, A. L. Simpson, J. N. Leal, M. I. D’Angelica, and R. K. G. Do, “Assessing splenic enlargement on CT by unidimensional measurement changes in patients with colorectal liver metastases,” Abdom. Imaging, vol. 40, no. 7, pp. 2338–2344, Oct. 2015, doi: 10.1007/s00261-015-0451-7.

[100]     E. Sadot et al., “Cholangiocarcinoma: Correlation between Molecular Profiling and Imaging Phenotypes,” PLOS ONE, vol. 10, no. 7, p. e0132953, Jul. 2015, doi: 10.1371/journal.pone.0132953.

[101]     A. L. Simpson et al., “Chemotherapy-Induced Splenic Volume Increase Is Independently Associated with Major Complications after Hepatic Resection for Metastatic Colorectal Cancer,” J. Am. Coll. Surg., vol. 220, no. 3, pp. 271–280, Mar. 2015, doi: 10.1016/j.jamcollsurg.2014.12.008.

[102]     A. L. Simpson et al., “Texture Analysis of Preoperative CT Images for Prediction of Postoperative Hepatic Insufficiency: A Preliminary Study,” J. Am. Coll. Surg., vol. 220, no. 3, pp. 339–346, Mar. 2015, doi: 10.1016/j.jamcollsurg.2014.11.027.

[103]     L. W. Clements, J. A. Collins, Y. Wu, A. L. Simpson, W. R. Jarnagin, and M. I. Miga, “Validation of model-based deformation correction in image-guided liver surgery via tracked intraoperative ultrasound: preliminary method and results,” in Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2015, pp. 208–216. doi: 10.1117/12.2082940.

[104]     U. Leung, A. L. Simpson, L. B. Adams, W. R. Jarnagin, M. I. Miga, and T. P. Kingham, “Image guidance improves localization of sonographically occult colorectal liver metastases,” in Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2015, pp. 389–393. doi: 10.1117/12.2082949.

[105]     A. L. Simpson, W. R. Jarnagin, and M. I. D’Angelica, “Hepatic Resection Planning in the Modern Era: In reply to Mise and colleagues,” J. Am. Coll. Surg., vol. 219, no. 6, p. 1195, Dec. 2014, doi: 10.1016/j.jamcollsurg.2014.09.013.

[106]     U. Leung et al., “Remnant Growth Rate after Portal Vein Embolization Is a Good Early Predictor of Post-Hepatectomy Liver Failure,” J. Am. Coll. Surg., vol. 219, no. 4, pp. 620–630, Oct. 2014, doi: 10.1016/j.jamcollsurg.2014.04.022.

[107]     A. L. Simpson, B. Ma, E. M. Vasarhelyi, D. P. Borschneck, R. E. Ellis, and A. James Stewart, “Computation and visualization of uncertainty in surgical navigation,” Int. J. Med. Robot., vol. 10, no. 3, pp. 332–343, 2014, doi: 10.1002/rcs.1541.

[108]     A. L. Simpson et al., “Liver Planning Software Accurately Predicts Postoperative Liver Volume and Measures Early Regeneration,” J. Am. Coll. Surg., vol. 219, no. 2, pp. 199–207, Aug. 2014, doi: 10.1016/j.jamcollsurg.2014.02.027.

[109]     A. L. Simpson et al., “Evaluation of Conoscopic Holography for Estimating Tumor Resection Cavities in Model-Based Image-Guided Neurosurgery,” IEEE Trans. Biomed. Eng., vol. 61, no. 6, pp. 1833–1843, Jun. 2014, doi: 10.1109/TBME.2014.2308299.

[110]     T. S. Pheiffer, R. C. Thompson, D. C. Rucker, A. L. Simpson, and M. I. Miga, “Model-Based Correction of Tissue Compression for Tracked Ultrasound in Soft Tissue Image-Guided Surgery,” Ultrasound Med. Biol., vol. 40, no. 4, pp. 788–803, Apr. 2014, doi: 10.1016/j.ultrasmedbio.2013.11.003.

[111]     A. L. Simpson, R. K. Do, E. P. Parada, M. I. Miga, and W. R. Jarnagin, “Texture feature analysis for prediction of postoperative liver failure prior to surgery,” in Medical Imaging 2014: Image Processing, SPIE, Mar. 2014, pp. 278–283. doi: 10.1117/12.2043055.

[112]     Y. Wu et al., “Registration of liver images to minimally invasive intraoperative surface and subsurface data,” in Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2014, pp. 228–235. doi: 10.1117/12.2044250.

[113]     A. L. Simpson et al., “Chemotherapy induced splenic volume increase is associated with major complications after hepatic resection for metastatic colorectal cancer,” J. Am. Coll. Surg., vol. 219, no. 4, p. e115, Oct. 2014, doi: 10.1016/j.jamcollsurg.2014.07.694.

[114]     K. Sun, T. S. Pheiffer, A. L. Simpson, J. A. Weis, R. C. Thompson, and M. I. Miga, “Near Real-Time Computer Assisted Surgery for Brain Shift Correction Using Biomechanical Models,” IEEE J. Transl. Eng. Health Med., vol. 2, pp. 1–13, 2014, doi: 10.1109/JTEHM.2014.2327628.

[115]     I. Chen, R. E. Ong, A. L. Simpson, K. Sun, R. C. Thompson, and M. I. Miga, “Integrating Retraction Modeling Into an Atlas-Based Framework for Brain Shift Prediction,” IEEE Trans. Biomed. Eng., vol. 60, no. 12, pp. 3494–3504, Dec. 2013, doi: 10.1109/TBME.2013.2272658.

[116]     D. C. Rucker et al., “A Mechanics-Based Nonrigid Registration Method for Liver Surgery Using Sparse Intraoperative Data,” IEEE Trans. Med. Imaging, vol. 33, no. 1, pp. 147–158, Jan. 2014, doi: 10.1109/TMI.2013.2283016.

[117]     J. Burgner et al., “A study on the theoretical and practical accuracy of conoscopic holography-based surface measurements: toward image registration in minimally invasive surgery,” Int. J. Med. Robot., vol. 9, no. 2, pp. 190–203, 2013, doi: 10.1002/rcs.1446.

[118]     T. S. Pheiffer, A. L. Simpson, J. E. Ondrake, and M. I. Miga, “Geometric reconstruction using tracked ultrasound strain imaging,” in Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2013, pp. 360–365. doi: 10.1117/12.2008045.

[119]     A. N. Kumar, T. S. Pheiffer, A. L. Simpson, R. C. Thompson, M. I. Miga, and B. M. Dawant, “Phantom-based comparison of the accuracy of point clouds extracted from stereo cameras and laser range scanner,” in Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2013, pp. 551–563. doi: 10.1117/12.2008036.

[120]     A. L. Simpson, N. P. Dillon, M. I. Miga, and B. Ma, “A framework for measuring TRE at the tip of an optically tracked pointing stylus,” in Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2013, pp. 292–297. doi: 10.1117/12.2008507.

[121]     I. Chen, A. L. Simpson, K. Sun, R. C. Thompson, and M. I. Miga, “Sensitivity analysis and automation for intraoperative implementation of the atlas-based method for brain shift correction,” in Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2013, pp. 213–224. doi: 10.1117/12.2007420.

[122]     D. C. Rucker, Y. Wu, J. E. Ondrake, T. S. Pheiffer, A. L. Simpson, and M. I. Miga, “Nonrigid liver registration for image-guided surgery using partial surface data: a novel iterative approach,” in Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Mar. 2013, pp. 86–91. doi: 10.1117/12.2007991.

[123]     A. L. Simpson, P. Dumpuri, J. E. Ondrake, J. A. Weis, W. R. Jarnagin, and M. I. Miga, “Preliminary study of a novel method for conveying corrected image volumes in surgical navigation,” Int. J. Med. Robot., vol. 9, no. 1, pp. 109–118, 2013, doi: 10.1002/rcs.1459.

[124]     A. L. Simpson et al., “Comparison Study of Intraoperative Surface Acquisition Methods for Surgical Navigation,” IEEE Trans. Biomed. Eng., vol. 60, no. 4, pp. 1090–1099, Apr. 2013, doi: 10.1109/TBME.2012.2215033.

[125]     M. J. Shannon et al., “Initial study of breast tissue retraction toward image guided breast surgery,” in Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Feb. 2012, pp. 630–635. doi: 10.1117/12.912860.

[126]     K. E. Miller, J. E. Ondrake, T. S. Pheiffer, A. L. Simpson, and M. I. Miga, “Utilizing ultrasound as a surface digitization tool in image guided liver surgery,” in Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Feb. 2012, pp. 1012–1018. doi: 10.1117/12.912373.

[127]     A. L. Simpson et al., “Intraoperative brain tumor resection cavity characterization with conoscopic holography,” in Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, Feb. 2012, pp. 913–920. doi: 10.1117/12.911926.

[128]     P. J. Swaney et al., “Tracked 3D ultrasound targeting with an active cannula,” in Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE, 2012, pp. 251–259.

[129]     T. S. Pheiffer, A. L. Simpson, B. Lennon, R. C. Thompson, and M. I. Miga, “Design and evaluation of an optically-tracked single-CCD laser range scanner,” Med. Phys., vol. 39, no. 2, pp. 636–642, 2012, doi: 10.1118/1.3675397.

[130]     A. L. Simpson, P. Dumpuri, W. R. Jarnagin, and M. I. Miga, “Model-Assisted Image-Guided Liver Surgery Using Sparse Intraoperative Data,” in Soft Tissue Biomechanical Modeling for Computer Assisted Surgery, Y. Payan, Ed., in Studies in Mechanobiology, Tissue Engineering and Biomaterials. , Berlin, Heidelberg: Springer, 2012, pp. 7–40. doi: 10.1007/8415_2012_117.

[131]     S. Ding, M. I. Miga, T. S. Pheiffer, A. L. Simpson, R. C. Thompson, and B. M. Dawant, “Tracking of Vessels in Intra-Operative Microscope Video Sequences for Cortical Displacement Estimation,” IEEE Trans. Biomed. Eng., vol. 58, no. 7, pp. 1985–1993, Jul. 2011, doi: 10.1109/TBME.2011.2112656.

[132]     C. Glisson, R. Ong, A. Simpson, P. Clark, S. D. Herrell, and R. Galloway, “The use of virtual fiducials in image-guided kidney surgery,” in Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, SPIE, Mar. 2011, pp. 21–29. doi: 10.1117/12.877092.

[133]     T. S. Pheiffer, B. Lennon, A. L. Simpson, and M. I. Miga, “Development of a novel laser range scanner,” in Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, SPIE, Mar. 2011, pp. 644–651. doi: 10.1117/12.878390.

[134]     M. I. Miga, P. Dumpuri, A. L. Simpson, J. A. Weis, and W. R. Jarnagin, “The sparse data extrapolation problem: strategies for soft-tissue correction for image-guided liver surgery,” in Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, SPIE, Mar. 2011, pp. 93–100. doi: 10.1117/12.878696.

[135]     A. L. Simpson, B. Ma, R. E. Ellis, A. J. Stewart, and M. I. Miga, “Uncertainty propagation and analysis of image-guided surgery,” in Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, SPIE, Mar. 2011, pp. 138–144. doi: 10.1117/12.878774.

[136]     A. L. Simpson, B. Ma, B. Slagel, D. P. Borschneck, and R. E. Ellis, “Computer-assisted distraction osteogenesis by Ilizarov’s method,” Int. J. Med. Robot., vol. 4, no. 4, pp. 310–320, 2008, doi: 10.1002/rcs.211.

[137]     A. L. Simpson, B. Ma, B. Slagel, D. P. Borschneck, and R. E. Ellis, “COMPUTER-ASSISTED DISTRACTION OSTEOGENESIS USING THE TAYLOR FRAME: INITIAL CLINICAL EXPERIENCES,” Orthop. Proc., vol. 90-B, no. SUPP_III, pp. 558–558, Aug. 2008, doi: 10.1302/0301-620X.90BSUPP_III.0900558b.

[138]     A. L. Simpson, B. Ma, E. C. S. Chen, R. E. Ellis, and A. J. Stewart, “Computation and Validation of Intra- operative Camera Uncertainty,” in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug. 2007, pp. 479–482. doi: 10.1109/IEMBS.2007.4352327.

[139]     B. Ma, A. L. Simpson, and R. E. Ellis, “Innovative Clinical and Biological Applications–III-Proof of Concept of a Simple Computer-Assisted Technique for Correcting Bone Deformities,” Lect. Notes Comput. Sci., vol. 4792, pp. 935–942, 2007.

[140]     B. Ma, A. L. Simpson, and R. E. Ellis, “Proof of Concept of a Simple Computer–Assisted Technique for Correcting Bone Deformities,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007, N. Ayache, S. Ourselin, and A. Maeder, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2007, pp. 935–942. doi: 10.1007/978-3-540-75759-7_113.

[141]     A. Simpson, B. Ma, D. Borschneck, and R. Ellis, “Computer-assisted distraction osteogenesis by Ilizarov’s method: a case report,” Int. J. Comput. Assist. Radiol. Surg., vol. 1, p. 247, 2006.

[142]     A. L. Simpson, B. Ma, E. C. S. Chen, R. E. Ellis, and A. J. Stewart, “Using Registration Uncertainty Visualization in a User Study of a Simple Surgical Task,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006, R. Larsen, M. Nielsen, and J. Sporring, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2006, pp. 397–404. doi: 10.1007/11866763_49.

[143]     A. L. Simpson, B. Ma, D. P. Borschneck, and R. E. Ellis, “Computer–Assisted Deformity Correction Using the Ilizarov Method,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005, J. S. Duncan and G. Gerig, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2005, pp. 459–466. doi: 10.1007/11566465_57.