Automatic Population of Structured Reports from Narrative Pathology Reports

There are a number of advantages for the use of structured pathology reports: they can ensure the accuracy and completeness of pathology reporting; it is easier for the referring doctors to glean pertinent information from them. The goal of this thesis is to extract pertinent information from free-text pathology reports and automatically populate structured reports for cancer diseases and identify the commonalities and differences in processing principles to obtain maximum accuracy. Three pathology corpora were annotated with entities and relationships between the entities in this study, namely the melanoma corpus, the colorectal cancer corpus and the lymphoma corpus. A supervised machine-learning based-approach, utilising conditional random fields learners, was developed to recognise medical entities from the corpora. By feature engineering, the best feature configurations were attained, which boosted the F-scores significantly from 4.2% to 6.8% on the training sets. Without proper negation and uncertainty detection, the quality of the structured reports will be diminished. The negation and uncertainty detection modules were built to handle this problem. The modules obtained overall F-scores ranging from 76.6% to 91.0% on the test sets. A relation extraction system was presented to extract four relations from the lymphoma corpus. The system achieved very good performance on the training set, with 100% F-score obtained by the rule-based module and 97.2% F-score attained by the support vector machines classifier. Rule-based approaches were used to generate the structured outputs and populate them to predefined templates. The rule-based system attained over 97% F-scores on the training sets. A pipeline system was implemented with an assembly of all the components described above. It achieved promising results in the end-to-end evaluations, with 86.5%, 84.2% and 78.9% F-scores on the melanoma, colorectal cancer and lymphoma test sets respectively

AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software.

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (

Lexical patterns, features and knowledge resources for coreference resolution in clinical notes

Generation of entity coreference chains provides a means to extract linked narrative events from clinical notes, but despite being a well-researched topic in natural language processing, general- purpose coreference tools perform poorly on clinical texts. This paper presents a knowledge-centric and pattern-based approach to resolving coreference across a wide variety of clinical records comprising discharge summaries, progress notes, pathology, radiology and surgical reports from two corpora (Ontology Development and Information Extraction (ODIE) and i2b2/VA). In addition, a method for generating coreference chains using progressively pruned linked lists is demonstrated that reduces the search space and facilitates evaluation by a number of metrics. Independent evaluation results show an F-measure for each corpus of 79.2% and 87.5%, respectively, which offers performance at least as good as human annotators, greatly increased performance over general- purpose tools, and improvement on previously reported clinical coreference systems. The system uses a number of open-source components that are available to download

Cervical nodal volume for prognostication and risk stratification of patients with nasopharyngeal carcinoma, and implications on the TNM-staging system

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Selective attention to sound features mediates cross-modal activation of visual cortices.

Contemporary schemas of brain organization now include multisensory processes both in low-level cortices as well as at early stages of stimulus processing. Evidence has also accumulated showing that unisensory stimulus processing can result in cross-modal effects. For example, task-irrelevant and lateralised sounds can activate visual cortices; a phenomenon referred to as the auditory-evoked contralateral occipital positivity (ACOP). Some claim this is an example of automatic attentional capture in visual cortices. Other results, however, indicate that context may play a determinant role. Here, we investigated whether selective attention to spatial features of sounds is a determining factor in eliciting the ACOP. We recorded high-density auditory evoked potentials (AEPs) while participants selectively attended and discriminated sounds according to four possible stimulus attributes: location, pitch, speaker identity or syllable. Sound acoustics were held constant, and their location was always equiprobable (50% left, 50% right). The only manipulation was to which sound dimension participants attended. We analysed the AEP data from healthy participants within an electrical neuroimaging framework. The presence of sound-elicited activations of visual cortices depended on the to-be-discriminated, goal-based dimension. The ACOP was elicited only when participants were required to discriminate sound location, but not when they attended to any of the non-spatial features. These results provide a further indication that the ACOP is not automatic. Moreover, our findings showcase the interplay between task-relevance and spatial (un)predictability in determining the presence of the cross-modal activation of visual cortices

Fetlock Parameters Development on Dorsopalmar Radiographs in the Equine Forelimb

Several reports have discussed possible bony morphological causes of fetlock pathology but without relating them to its morphometry. Radiographic measurement is widely used in constructing numerical databases of bone morphometry. Such measurements would not be reliable unless all factors affecting the radiographs were considered. Therefore, this study aimed to establish a specific dorsopalmar view (DP) for fetlock radiographic measurements, and then to develop reliable and repeatable fetlock parameters that represent fetlock conformation on those DP radiographs. Ten cadaveric forelimbs from ten adult horses were cut at the distal third of the radius and mounted in a normal posture for DP radiography. Specific features on fetlock bones were used as landmarks to identify the DP at zero degrees. Other bony features were selected as landmarks for developing fetlock parameters on these radiographs. Twenty-seven parameters were designed in the form of angles (12) and ratios (15). The repeatability and consistency of each parameter was tested. A specific fetlock DP view was determined at zero degrees based on certain landmarks. All angular and ratio parameters showed high reliability and consistency in their measurements. The established parameters provide an opportunity to test the relationship between fetlock morphometrics and performance, or the likelihood of certain pathologies

Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI

The clinical management of breast cancer depends on an accurate understanding of the tumor and its anatomical context to adjacent tissues and landmark structures. This context may be provided by semantic segmentation methods; however, previous works have been largely limited to a singular focus on the tumor alone and rarely other tissue types. In contrast, we present a method that exploits tissue-tissue interactions to accurately segment every major tissue type in the breast including: chest wall, skin, adipose tissue, fibroglandular tissue, vasculature and tumor via standard-of-care Dynamic Contrast Enhanced MRI. Comparing our method to prior state-of-the-art, we achieved a superior Dice score on tumor segmentation while maintaining competitive performance on other studied tissues across multiple institutions. Briefly, our method proceeds by localizing the tumor using 2D object detectors, then segmenting the tumor and surrounding tissues independently using two 3D U-nets, and finally integrating these results while mitigating false positives by checking for anatomically plausible tissue-tissue contacts. The object detection models were pre-trained on ImageNet and COCO, and operated on MIP (maximum intensity projection) images in the axial and sagittal planes, establishing a 3D tumor bounding box. By integrating multiple relevant peri-tumoral tissues, our work enables clinical applications in breast cancer staging, prognosis and surgical planning.Comment: 9 pages, 2 figures, to appear in SPIE: Medical Imaging 202

Differential neural encoding of sensorimotor and visual body representations.

Sensorimotor processing specifically impacts mental body representations. In particular, deteriorated somatosensory input (as after complete spinal cord injury) increases the relative weight of visual aspects of body parts' representations, leading to aberrancies in how images of body parts are mentally manipulated (e.g. mental rotation). This suggests that a sensorimotor or visual reference frame, respectively, can be relatively dominant in local (hands) versus global (full-body) bodily representations. On this basis, we hypothesized that the recruitment of a specific reference frame could be reflected in the activation of sensorimotor versus visual brain networks. To this aim, we directly compared the brain activity associated with mental rotation of hands versus full-bodies. Mental rotation of hands recruited more strongly the supplementary motor area, premotor cortex, and secondary somatosensory cortex. Conversely, mental rotation of full-bodies determined stronger activity in temporo-occipital regions, including the functionally-localized extrastriate body area. These results support that (1) sensorimotor and visual frames of reference are used to represent the body, (2) two distinct brain networks encode local or global bodily representations, and (3) the extrastriate body area is a multimodal region involved in body processing both at the perceptual and representational level