Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers

Deep learning-based body part recognition algorithm for three-dimensional medical images

BACKGROUND

The automatic recognition of human body parts in three-dimensional (3D) 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 pre-classification 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 post-processing 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 mm 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. This article is protected by copyright. All rights reserved.

High throughput image labeling on chest computed tomography by deep learning

When mining image data from PACs or clinical trials or processing large volumes of data without curation, the relevant scans must be identified among irrelevant or redundant data. Only images acquired with appropriate technical factors, patient positioning, and physiological conditions may be applicable to a particular image processing or machine learning task. Automatic labeling is important to make big data mining practical by replacing conventional manual review of every single-image series. Digital imaging and communications in medicine headers usually do not provide all the necessary labels and are sometimes incorrect. We propose an image-based high throughput labeling pipeline using deep learning, aimed at identifying scan direction, scan posture, lung coverage, contrast usage, and breath-hold types. They were posed as different classification problems and some of them involved further segmentation and identification of anatomic landmarks. Images of different view planes were used depending on the specific classification problem. All of our models achieved accuracy > 99 % on test set across different tasks using a research database from multicenter clinical trials.