Background The automatic recognition of human body parts in three‐dimensional medical images is important in many clinical applications. However, methods presented in prior studies have mainly classified each two‐dimensional (2D) slice independently rather than recognizing a batch of consecutive slices as a specific body part. Purpose In this study, we aim to develop a deep learning‐based method designed to automatically divide computed tomography (CT) and magnetic resonance imaging (MRI) scans into five consecutive body parts: head, neck, chest, abdomen, and pelvis. Methods A deep learning framework was developed to recognize body parts in two stages. In the first preclassification stage, a convolutional neural network (CNN) using the GoogLeNet Inception v3 architecture and a long short‐term memory (LSTM) network were combined to classify each 2D slice; the CNN extracted information from a single slice, whereas the LSTM employed rich contextual information among consecutive slices. In the second postprocessing stage, the input scan was further partitioned into consecutive body parts by identifying the optimal boundaries between them based on the slice classification results of the first stage. To evaluate the performance of the proposed method, 662 CT and 1434 MRI scans were used. Results Our method achieved a very good performance in 2D slice classification compared with state‐of‐the‐art methods, with overall classification accuracies of 97.3% and 98.2% for CT and MRI scans, respectively. Moreover, our method further divided whole scans into consecutive body parts with mean boundary errors of 8.9 and 3.5 mm for CT and MRI data, respectively. Conclusions The proposed method significantly improved the slice classification accuracy compared with state‐of‐the‐art methods, and further accurately divided CT and MRI scans into consecutive body parts based on the results of slice classification. The developed method can be employed as an important step in various computer‐aided diagnosis and medical image analysis schemes.

Blang: Bayesian Modeling of General Data StructuresCorrectness: Bayesian inference software is notoriously difficult to implement. An example from the tip of the iceberg is shown in Geweke (2004), which identifies software bugs and erroneous results in earlier published studies. We address this issue using a marriage of statistical theory and software engineering methodology, such as compositionality and unit testing.Ease of use: Blang uses a familiar BUGS-like syntax and it is designed to be integrated well in modern data science workflows (input in tidy format, Wickham 2014, samples output in tidy format).Generality: As a programming language, Blang is Turing-complete and equipped with an open type system, as well as facilities to quickly develop and test sampling algorithms for new types. By an open type system, we mean that the set of types is not limited to integers and real numbers, and can be arbitrary classes. Blang does not fully automate the process of posterior sampling from user-defined types but instead greatly facilitates the development, composition and sharing of custom sampling algorithms. Computational scalability:The language is designed to ensure that state-of-the-art Monte Carlo methods can be utilized. In particular, we made certain trade-offs to ensure that a well-behaved continuum of distributions can be automatically created. This is complemented with methods that extend existing PPL strategies to the combinatorial space, for example a code scoping analysis to discover sparsity patterns with arbitrary types, as well as built-in support for parallelization to arbitrary numbers of cores. License, source, version and documentation availabilityBlang is free and open source. The language and SDK are available under a permissive BSD 2-Clause license. The relevant GitHub repositories are linked at https://github. com/UBC-Stat-ML/blangDoc. Online documentation is available at https://www.stat.ubc. ca/~bouchard/blang/, including Javadoc pages at https://www.stat.ubc.ca/~bouchard/ blang/Javadoc.html. TutorialThis section aims to introduce readers to Blang by presenting a minimal working example. We begin with instructions for performing inference on a simple model using the commandline interface (CLI). Realistic applications are demonstrated in Sections 6 and 9. Advanced tutorials can be found in Appendix A. Installing Blang's command-line interfaceWe provide instructions here for installing and using Blang via the CLI. Alternative Blang interfaces include an integrated development environment (IDE) as well as a Web interface, both detailed in Section 10.1. Instructions are also available from the documentation website (https://www.stat.ubc.ca/~bouchard/blang/) under the link Tools. Additionally, an R (R Core Team 2022) and Python (van Rossum et al. 2011) interface to Blang are currently under development. 1 8 See https://github.com/UBC-Stat-ML/JSSBlangCode/blob/master/reproduction_material/example/ jss/gmm/MixtureModel.bl. Complete and commented implementations in this section are available in the...

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