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.

Electrochemical CO2 reduction to value-added chemicals or fuels is a prospective strategy for facilitating closing carbon loop. However, there still exist challenges in developing efficient catalysts and optimizing the electrolyzer...

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