Versatile anomaly detection method for medical images with semi-supervised flow-based generative models.

PURPOSE: Radiologists interpret many medical images and clinical practice demands timely interpretation, resulting in a heavy workload. To reduce the workload, here we formulate and validate a method that can handle different types of medical image and can detect virtually all types of lesion in a medical image. For the first time, we show that two flow-based deep generative (FDG) models can predict the logarithm posterior probability in a semi-supervised approach. METHODS: We adopt two FDG models in conjunction with Bayes' theorem to predict the logarithm posterior probability that a medical image is normal. We trained one of the FDG models with normal images and the other FDG model with normal and non-normal images. RESULTS: We validated the method using two types of medical image: chest X-ray images (CXRs) and brain computed tomography images (BCTs). The area under the receiver operating characteristic curve for pneumonia-like opacities in CXRs was 0.839 on average, and for infarction in BCTs was 0.904. CONCLUSION: We formulated a method of predicting the logarithm posterior probability using two FDG models. We validated that the method can detect abnormal findings in CXRs and BCTs with both an acceptable performance for testing and a comparatively light workload for training.

Novel platform for development, training, and validation of computer-assisted detection/diagnosis software.

PURPOSE: To build a novel, open-source, purely web-based platform system to address problems in the development and clinical use of computer-assisted detection/diagnosis (CAD) software. The new platform system will replace the existing system for the development and validation of CAD software, Clinical Infrastructure for Radiologic Computation of United Solutions (CIRCUS). METHODS: In our new system, the two top-level applications visible to users are the web-based image database (CIRCUS DB; database) and the Docker plug-in-based CAD execution platform (CIRCUS CS; clinical server). These applications are built on top of a shared application programming interface server, a three-dimensional image viewer component, and an image repository. RESULTS: We successfully installed our new system into a Linux server at two clinical sites. A total of 1954 cases were registered in CIRCUS DB. We have been utilizing CIRCUS CS with four Docker-based CAD plug-ins. CONCLUSIONS: We have successfully built a new version of the CIRCUS system. Our platform was successfully implemented at two clinical sites, and we plan to publish it as an open-source software project.

Ghost cytometry.

Ghost imaging is a technique used to produce an object's image without using a spatially resolving detector. Here we develop a technique we term "ghost cytometry," an image-free ultrafast fluorescence "imaging" cytometry based on a single-pixel detector. Spatial information obtained from the motion of cells relative to a static randomly patterned optical structure is compressively converted into signals that arrive sequentially at a single-pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random pattern allows us to computationally reconstruct cell morphology. More importantly, we show that applying machine-learning methods directly on the compressed waveforms without image reconstruction enables efficient image-free morphology-based cytometry. Despite a compact and inexpensive instrumentation, image-free ghost cytometry achieves accurate and high-throughput cell classification and selective sorting on the basis of cell morphology without a specific biomarker, both of which have been challenging to accomplish using conventional flow cytometers.

Inter-observer variance and the need for standardization in the morphological classification of myelodysplastic syndrome.

In this era of genome medicine, the sub-classification of myeloid neoplasms, including myelodysplastic syndrome (MDS), is now supported by genetic testing in selected cases. However, as the initial suspicion and primary diagnosis of the disease still largely relies on morphological features and numbers of hematopoietic cells, the establishment of a uniform diagnostic basis, especially for cell morphology, is essential. In this study, we collected nearly 100,000 hematopoietic cell images from 499 peripheral blood smear specimens from patients with MDS and used these to evaluate the standardization of morphological classification by medical technologists. The observers in this study ranged between two to eleven for each image, and the images were classified according to MDS criteria through a web-based system. We found considerable inter-observer variance in the assessment of dysplastic features. Observers did not recognize cytoplasmic hypo-granularity unless almost all granules in neutrophils were absent. Pseudo Pelger-Huët anomalies were also often overlooked, except for cells with a very typical "pince-nez" appearance. Taken together, this study suggests a requirement for further standardization in terms of morphological cell classification, and a need for the development of automatic cell classification-supporting devices for the accurate diagnosis of MDS.