LHNCBC

Atlas-based lung boundary detection module (MATLAB Code)

We present to you software developed as part of the following article:

Candemir S, Jaeger S, Palaniappan K, Musco JP, Singh RK, Xue Z, Karargyris A, Antani S, Thoma G, McDonald CJ. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging. 2014 Feb; 33(2):577-90. doi: 10.1109/TMI.2013.2290491. PMID: 24239990

We request that you cite the paper if this code is used for publications or product.

The software contains the following assets:

- LungSegment_module.m: performs the lung segmentation on CXRs.

- Patient_Xrays: folder contain patient X-ray to be segmented. Please locate your test X-rays in this folder. The X-ray image can be in DICOM, TIFF, PNG, BMP, or JPEG file formats.

- Model_Xrays: folder contain example X-rays, their corresponding lung masks, vertical, and horizontal profiles. These X-rays and lung masks are used during registration process.

- Model_h.mat and Model_v.mat: precomputed profiles for model x-rays.

- TEST.m: to test the system, please run this function.

- subfunctions: CEB_polygionCurveEvolution.m, cebShape2Spline.m, f_removeBlackBorderds.m, f_RemoveSmallSegments.m, FindSimilarCXR.m, fitSpline.m, read_Xray.m, read_Xray.m, read_X_Mask.m, RegisterModel.m, SmoothBoundary.m (please refer code comments for these functions. )

The system is developed using MATLAB version 8.4 on 64-bit Intel architecture running Windows 7 operating system.

- Employed functions: mexDenseSIFT(m, mex), mesDiscreteFlow(m,mex), SIFTflowc2f.m functions are employed from the “SIFT Flow: Dense Correspondence across Scenes and Its applications”. Please use the http://people.csail.mit.edu/celiu/SIFTflow/ website to download the latest version of these functions. Do not forget citing the SIFTFlow paper.

How to run:

Download and unpack the zip file containing the code here.
Locate your CXRs in the Patient X-ray folder.
Run the TEST.m function. The binary lung masks will be computed for each patient X-ray. The masks will be the same size as patient X-ray.
Adjust parameters to obtain better results for your CXRs.

Contact: Stefan Jaeger

Download

Many existing electronic health record (EHR) systems contain clinical information encoded in ICD-9-CM. To facilitate migration to SNOMED CT as the primary clinical terminology for patient problems (diseases and conditions), it is desirable that the legacy ICD-9-CM data be translated to SNOMED CT. This will make it possible to compare newly collected data with historic data, and will also allow the EHR to make use of SNOMED CT to provide clinical decision support and other functions. The goal of the ICD-9-CM to SNOMED CT Map (herein referred to as “the Map”) is to facilitate the translation of legacy data and the transition to prospective use of SNOMED CT for patient problem lists. Note that this Map is not the same as, and serves different purposes from, the SNOMED CT to ICD-9-CM Map.

The most useful mappings are the one-to-one maps, in which a single SNOMED CT concept can be used to represent the full meaning of an ICD-9-CM code. This allows the automatic translation of ICD-9-CM codes into SNOMED CT codes without loss of meaning. The Map tries to identify as many one-to-one maps as possible, however, due to the differences between the two coding systems, one-to-one maps cannot be found for some ICD-9-CM codes. This difference is usually due to one of two reasons. Firstly, in ICD-9-CM, some codes are “catch-all” codes that encompass heterogeneous diseases or conditions (e.g. pneumonia due to other specified bacteria). These codes, commonly known as “NEC codes” (not elsewhere classified codes), will not have one-to-one maps because of their nature. Secondly, since SNOMED CT is more granular than ICD-9-CM in most disease areas, some ICD-9-CM diseases or conditions are further refined as more specific concepts in SNOMED CT. For such cases, it is not possible to map to a more specific SNOMED CT concept without the input of additional information.

The Map is published in two separate files, one containing the one-to-one maps, and the other the one-to-many maps. Also included in the files are the usage frequency of the ICD-9-CM codes, and the usage frequency of the SNOMED CT concepts from the CORE Problem List Subset data. The latter information can help users to identify the more commonly used SNOMED CT targets in the one-to-many maps.

Mapping Methodology
Two lists were obtained from the Centers for Medicare & Medicaid Services (CMS), covering commonly used ICD-9-CM codes in short-stay and outpatient hospitals respectively, for the year 2009. SNOMED CT maps for the ICD-9-CM codes in the lists were derived primarily from two existing knowledge sources: the synonymy between ICD-9-CM and SNOMED CT terms in the Unified Medical Language System (UMLS), and the SNOMED CT to ICD-9-CM Cross Maps published in the International release of SNOMED CT. The choice of target SNOMED CT codes was limited to concepts in three hierarchies: Clinical finding, Situation with explicit context, and Events. One-to-one maps identified by UMLS synonymy were not manually validated. One-to-many maps that were algorithmically identified which involved less than 5 SNOMED CT targets were manually reviewed, with the intention to reduce them to one-to-one maps if possible. ICD-9-CM codes with no maps, or one-to-many maps involving a large number of targets were not manually reviewed.

Dataset: http://www.nlm.nih.gov/research/umls/mapping_projects/icd9cm_to_snomedct.html

Malaria Screener datasheet Details of datasets and download links

This page hosts a repository of P. vivax and P. falciparum images in both thin and thick blood smears from the Malaria Screener research activity.

To reduce the burden for microscopists in resource-constrained regions and improve diagnostic accuracy, researchers at the Lister Hill National Center for Biomedical Communications (LHNCBC), part of National Library of Medicine (NLM), have developed a mobile application, called “Malaria Screener”, which runs on a standard Android smartphone attached to a conventional light microscope. The smartphone's built-in camera acquired images of slides for each microscopic field of view. The images were manually annotated by an expert slide reader at the Mahidol-Oxford Tropical Medicine Research Unit in Bangkok, Thailand. The de-identified images and annotations are archived at NLM (IRB#12972).

The dataset includes five main parts:

Giemsa-stained thick blood smear slides from 150 P. falciparum-infected patients were collected and photographed at Chittagong Medical College Hospital, Bangladesh. We have developed the first deep learning method that can detect P. falciparum parasites in thick blood smear images and can run on smartphones, which consists of two modules: an intensity-based Iterative Global Minimum Screening (IGMS) module for parasite candidate screening and a customized CNN classifier for final classification. The data was published along with the following publication:

Yang F, Poostchi M, Yu H, Zhou Z, Silamut K, Yu J, Maude RJ, Jaeger S, Antani S. Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears. IEEE J Biomed Health Inform. 2020 May;24(5):1427-1438. (URL: https://ieeexplore.ieee.org/document/8846750 )
Giemsa-stained thick blood smear slides from 150 P. vivax-infected patients and 50 uninfected patients were collected and photographed at Chittagong Medical College Hospital, Bangladesh. Based on a dataset of 350 malaria patients, we proposed PlasmodiumVF-Net to diagnose a patients as uninfected, P. vivax-infected, or P. falciparum-infected. The data was published along with the publication:

Kassim Y M, Yang F, Yu H, Maude R J, Jaeger S. Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images. Diagnostic, 11(11):1994, 2021. (URL: https://www.mdpi.com/2075-4418/11/11/1994 )
Giemsa-stained thin blood smear slides from 148 P. falciparum-infected, and 45 uninfected patients were collected and photographed at Chittagong Medical College Hospital, Bangladesh. We proposed RBCNet that consists of a U-Net first stage for cell-cluster or super pixel segmentation, followed by a second refinement stage Faster R-CNN for detecting small cell objects within the connected component clusters. The corresponding publication is:

Kassim YM, Palaniappan K, Yang F, Poostchi M, Palaniappan N, Maude RJ, Antani S, Jaeger S. Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears. IEEE J Biomed Health Inform. 2021 May;25(5):1735-1746. (URL: https://ieeexplore.ieee.org/document/9244549 )
Giemsa-stained thin blood smear slides from 171 P. vivax-infected patients were collected and photographed in Bangkok, Thailand. We developed a rapid and robust diagnosis system for the automated detection of P. vivax parasites using a cascaded YOLO model. This system consists of a YOLOv2 model and a classifier for hard-negative mining; see the following publication:

Yang F, Quizon N, Silamut K, Maude RJ, Jaeger S, Antani SK. Cascading YOLO: Automated Malaria Parasite Detection for Plasmodium Vivax in Thin Blood Smears. Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141Q (16 March 2020); (URL: https://doi.org/10.1117/12.2549701 )
We acquired cell images from 150 P. falciparum-infected and 50 uninfected patients in Giemsa-stained thin blood smears that were collected and photographed at Chittagong Medical College Hospital, Bangladesh. The dataset contains a total of 27,558 cell images with equal instances of parasitized and uninfected cells. An instance of how the patient-ID is encoded into the cell name is shown herewith: “P1” denotes the patient-ID for the cell labeled “C33P1thinF_IMG_20150619_114756a_cell_179.png”. We have also included the CSV files containing the Patient-ID to cell mappings for the parasitized and uninfected classes. The CSV file for the parasitized class contains 151 patient-ID entries. The slide images for the parasitized patient-ID “C47P8thinOriginal” are read from two different microscope models (Olympus and Motif). The CSV file for the uninfected class contains 201 entries since the normal cells from the infected patients’ slides are also in the normal cell category (151+50 = 201). Experiments with the data were reported in the following paper (PeerJ6:e4568):

Rajaraman S, Antani SK, Poostchi M, Silamut K, Hossain MA, Maude, RJ, Jaeger S, Thoma GR. (2018) Pre-trained convolutional neural networks as feature extractors toward improved Malaria parasite detection in thin blood smear images. (URL: https://doi.org/10.7717/peerj.4568 )

Each dataset includes a separate readme file describing the dataset and annotation files.

Malaria Screener datasheet Details of datasets and download links

Malaria Screener Github Repository Access source code of Malaria Screener

Malaria Screener App Download Malaria Screener from the Google Play store

Malaria project page

Mapping SNOMED CT codes to and from ICD codes

SNOMED CT is clinically-based, and oriented for direct use by healthcare providers, to document whatever is needed for patient care. ICD codes are oriented more for coding professionals to use after patient care has already been provided, for statistical data collection and billing. ICD codes lump less common diseases together in "catch-all" categories, for example, J15.8 Pneumonia due to other specified bacteria, which could result in loss of information. SNOMED Ct has more "granular" (specific) clinical coverage than ICD:SNOMED CT (clinical finding) has 100,000 codes, ICD-10-CM has 68,000 codes, and ICD-9-CM has 14,000 codes.

Due to the differences in granularity, emphasis and organizing principles between SNOMED CT and ICD-10-CM, it is not always possible to have a one-to-one map between a SNOMED CT concept and an ICD-10-CM code. To address this challenge, the SNOMED CT to ICD-10-CM Map follows an approach that is consistent with the approach used by the IHTSDO and WHO. When there is a need to choose between alternative ICD-10-CM codes, each possible target code is represented as a “map rule” (the essence of “rule-based mapping”). Related map rules are grouped into a “map group”. Map rules within a map group are evaluated in a prescribed order at run-time, based on contextual information and co-morbidities. Each map group will resolve to at most one ICD-10-CM code. In the event that a SNOMED CT concept requires more than one ICD-10-CM code to fully represent its meaning, the map will consist of multiple map groups.

We have created the SNOMED CT to ICD-10-CM Map to support semi-automated generation of ICD-10-CM codes from clinical data encoded in SNOMED CT for reimbursement and statistical purposes.

Download: http://www.nlm.nih.gov/research/umls/mapping_projects/snomedct_to_icd10cm.html
- Latest release in September 2014 provides ICD-10-CM maps for 54,262 SNOMED CT concepts
- Third release (35,000 SNOMED CT concepts mapped to ICD-10-CM) is anticipated for June 2013.
- Second release was in July 2012 (15,000 SNOMED CT concepts mapped to ICD-10-CM).
- First release was in February 2012 (7000 SNOMED CT concepts mapped to ICD-10-CM).

DOWNLOADS

Chest X-ray Screening System: Segmentation Module – v3

Clinical Table Search Service

CORE Problem List of SNOMED CT

CSpell API

I-Magic SNOMED-CT to ICD-10-CM code mapping tool

ICD-9-CM to SNOMED CT Map

LexAccess API

LexCheck

Lexical Tools

Lexical Web Tools

Lung Masks of a Subset of Chest X-ray Images from the NLM Open-i® Indiana Dataset

Malaria Screener

MEDLINE n-gram set

MeSH on Demand

NLM-Scrubber

Nursing Problem List Subset of SNOMED CT

Prescribable RxNorm API

RxClass

RxMix

RxNav

RxNav-in-a-Box

RxNorm API

RxTerms

SNOMED CT Route of Administration Subset

SNOMED CT to ICD-10 Cross Map

SNOMED CT to ICD-10-CM Map

SPECIALIST Lexicon

Sub-Term Mapping Tools

Unified Code for Units of Measure (UCUM) Validation & Conversion API

Visible Human Project Reference Data Sets