Deep learning solution for medical image localization and orientation detection.

Magnetic Resonance (MR) imaging plays an important role in medical diagnosis and biomedical research. Due to the high in-slice resolution and low through-slice resolution nature of MR imaging, the usefulness of the reconstruction highly depends on the positioning of the slice group. Traditional clinical workflow relies on time-consuming manual adjustment that cannot be easily reproduced. Automation of this task can therefore bring important benefits in terms of accuracy, speed and reproducibility. Current auto-slice-positioning methods rely on automatically detected landmarks to derive the positioning, and previous studies suggest that a large, redundant set of landmarks are required to achieve robust results. However, a costly data curation procedure is needed to generate training labels for those landmarks, and the results can still be highly sensitive to landmark detection errors. More importantly, a set of anatomical landmark locations are not naturally produced during the standard clinical workflow, which makes online learning impossible. To address these limitations, we propose a novel framework for auto-slice-positioning that focuses on localizing the canonical planes within a 3D volume. The proposed framework consists of two major steps. A multi-resolution region proposal network is first used to extract a volume-of-interest, after which a V-net-like segmentation network is applied to segment the orientation planes. Importantly, our algorithm also includes a Performance Measurement Index as an indication of the algorithm's confidence. We evaluate the proposed framework on both knee and shoulder MR scans. Our method outperforms state-of-the-art automatic positioning algorithms in terms of accuracy and robustness.

Structure Correction for Robust Volume Segmentation in Presence of Tumors.

CNN based lung segmentation models in absence of diverse training dataset fail to segment lung volumes in presence of severe pathologies such as large masses, scars, and tumors. To rectify this problem, we propose a multi-stage algorithm for lung volume segmentation from CT scans. The algorithm uses a 3D CNN in the first stage to obtain a coarse segmentation of the left and right lungs. In the second stage, shape correction is performed on the segmentation mask using a 3D structure correction CNN. A novel data augmentation strategy is adopted to train a 3D CNN which helps in incorporating global shape prior. Finally, the shape corrected segmentation mask is up-sampled and refined using a parallel flood-fill operation. The proposed multi-stage algorithm is robust in the presence of large nodules/tumors and does not require labeled segmentation masks for entire pathological lung volume for training. Through extensive experiments conducted on publicly available datasets such as NSCLC, LUNA, and LOLA11 we demonstrate that the proposed approach improves the recall of large juxtapleural tumor voxels by at least 15% over state-of-the-art models without sacrificing segmentation accuracy in case of normal lungs. The proposed method also meets the requirement of CAD software by performing segmentation within 5 seconds which is significantly faster than present methods.

Impact of an educational intervention on errors in death certification: An observational study from the intensive care unit of a tertiary care teaching hospital.

BACKGROUND: A high incidence of errors occur while filling up death certificates in hospitals. The purpose of this study was to analyze the impact of an educational intervention on errors in death certification in an intensive care unit (ICU). Patients admitted to ICUs by virtue of being critically ill have a higher mortality than other hospitalized patients. This study was designed to see if any improvement could be brought about in filling death certificates. MATERIALS AND METHODS: Educating sessions, interactive workshops, and monthly audits for the department resident doctors were conducted. One hundred and fifty death certificates were audited for major and minor errors (75 before and 75 after the educational intervention) over a period of 18 months. Fisher's exact test was applied to statistically analyze the data. RESULTS: There was a significant decrease in major errors like mechanism without underlying cause of death (60.0 vs. 14.6%, P < 0.001), competing causes (88.0 vs. 13.3%, P < 0.001), and improper sequencing (89.3 vs. 36.0%, P < 0.001). There was also a significant decrease in minor errors such as use of abbreviations (89.3 vs. 29.3%, P < 0.001) and no time intervals (100.0 vs. 22.6%, P < 0.001). CONCLUSION: Authors conclude that death certification errors can be significantly reduced by educational interventional programs.