A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans

Liver cancer has high morbidity and mortality rates in the world. Multi-phase CT is a main medical imaging modality for detecting/identifying and diagnosing liver tumors. Automatically detecting and classifying liver lesions in CT images have the potential to improve the clinical workflow. This task remains challenging due to liver lesions' large variations in size, appearance, image contrast, and the complexities of tumor types or subtypes. In this work, we customize a multi-object labeling tool for multi-phase CT images, which is used to curate a large-scale dataset containing 1,631 patients with four-phase CT images, multi-organ masks, and multi-lesion (six major types of liver lesions confirmed by pathology) masks. We develop a two-stage liver lesion detection pipeline, where the high-sensitivity detecting algorithms in the first stage discover as many lesion proposals as possible, and the lesion-reclassification algorithms in the second stage remove as many false alarms as possible. The multi-sensitivity lesion detection algorithm maximizes the information utilization of the individual probability maps of segmentation, and the lesion-shuffle augmentation effectively explores the texture contrast between lesions and the liver. Independently tested on 331 patient cases, the proposed model achieves high sensitivity and specificity for malignancy classification in the multi-phase contrast-enhanced CT (99.2%, 97.1%, diagnosis setting) and in the noncontrast CT (97.3%, 95.7%, screening setting)

Effects of polymer molecular weight on relative oral bioavailability of curcumin

Yin-Meng Tsai,1 Wan-Ling Chang-Liao,1 Chao-Feng Chien,1 Lie-Chwen Lin,1,2 Tung-Hu Tsai,1,31Institute of Traditional Medicine, School of Medicine, National Yang-Ming University, 2National Research Institute of Chinese Medicine, 3Department of Education and Research, Taipei City Hospital, Taipei, TaiwanBackground: Polylactic-co-glycolic acid (PLGA) nanoparticles have been used to increase the relative oral bioavailability of hydrophobic compounds and polyphenols in recent years, but the effects of the molecular weight of PLGA on bioavailability are still unknown. This study investigated the influence of polymer molecular weight on the relative oral bioavailability of curcumin, and explored the possible mechanism accounting for the outcome.Methods: Curcumin encapsulated in low (5000–15,000) and high (40,000–75,000) molecular weight PLGA (LMw-NPC and HMw-NPC, respectively) were prepared using an emulsification-solvent evaporation method. Curcumin alone and in the nanoformulations was administered orally to freely mobile rats, and blood samples were collected to evaluate the bioavailability of curcumin, LMw-NPC, and HMw-NPC. An ex vivo experimental gut absorption model was used to investigate the effects of different molecular weights of PLGA formulation on absorption of curcumin. High-performance liquid chromatography with diode array detection was used for quantification of curcumin in biosamples.Results: There were no significant differences in particle properties between LMw-NPC and HMw-NPC, but the relative bioavailability of HMw-NPC was 1.67-fold and 40-fold higher than that of LMw-NPC and conventional curcumin, respectively. In addition, the mean peak concentration (Cmax) of conventional curcumin, LMw-NPC, and HMw-NPC was 0.028, 0.042, and 0.057 µg/mL, respectively. The gut absorption study further revealed that the HMw-PLGA formulation markedly increased the absorption rate of curcumin in the duodenum and resulted in excellent bioavailability compared with conventional curcumin and LMw-NPC.Conclusion: Our findings demonstrate that different molecular weights of PLGA have varying bioavailability, contributing to changes in the absorption rate at the duodenum. The results of this study provide the rationale for design of a nanomedicine delivery system to enhance the bioavailability of water-insoluble pharmaceutical compounds and functional foods.Keywords: absorption, duodenum, molecular weight, poly(lactic-co-glycolic acid), PLGA, relative oral bioavailabilit

Weakly Supervised Universal Fracture Detection in Pelvic X-rays

Hip and pelvic fractures are serious injuries with life-threatening complications. However, diagnostic errors of fractures in pelvic X-rays (PXRs) are very common, driving the demand for computer-aided diagnosis (CAD) solutions. A major challenge lies in the fact that fractures are localized patterns that require localized analyses. Unfortunately, the PXRs residing in hospital picture archiving and communication system do not typically specify region of interests. In this paper, we propose a two-stage hip and pelvic fracture detection method that executes localized fracture classification using weakly supervised ROI mining. The first stage uses a large capacity fully-convolutional network, i.e., deep with high levels of abstraction, in a multiple instance learning setting to automatically mine probable true positive and definite hard negative ROIs from the whole PXR in the training data. The second stage trains a smaller capacity model, i.e., shallower and more generalizable, with the mined ROIs to perform localized analyses to classify fractures. During inference, our method detects hip and pelvic fractures in one pass by chaining the probability outputs of the two stages together. We evaluate our method on 4 410 PXRs, reporting an area under the ROC curve value of 0.975, the highest among state-of-the-art fracture detection methods. Moreover, we show that our two-stage approach can perform comparably to human physicians (even outperforming emergency physicians and surgeons), in a preliminary reader study of 23 readers.Comment: MICCAI 2019 (early accept