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We define a representation framework for extracting spatial information fromradiology reports (Rad-SpRL). We annotated a total of 2000 chest X-ray reportswith 4 spatial roles corresponding to the common radiology entities. Our focusis on extracting detailed information of a radiologist's interpretationcontaining a radiographic finding, its anatomical location, correspondingprobable diagnoses, as well as associated hedging terms. For this, we propose adeep learning-based natural language processing (NLP) method involving bothword and character-level encodings. Specifically, we utilize a bidirectionallong short-term memory (Bi-LSTM) conditional random field (CRF) model forextracting the spatial roles. The model achieved average F1 measures of 90.28and 94.61 for extracting the Trajector and Landmark roles respectively whereasthe performance was moderate for Diagnosis and Hedge roles with average F1 of71.47 and 73.27 respectively. The corpus will soon be made available uponrequest.

Understanding Spatial Language in Radiology: Representation Framework,  Annotation, and Spatial Relation Extraction from Chest X-ray Reports using  Deep Learning