Log In Sign up
The largest database of trusted experimental protocols

Gt450 scanner

Manufactured by Leica
Visit Supplier

The GT450 scanner is a precision instrument designed for high-quality digitization of film and documents. It features a large-format scanning area and advanced optics to capture detailed images with excellent resolution and color accuracy. The GT450 is a reliable and versatile tool for professional archiving, preservation, and digital imaging applications.

Automatically generated - may contain errors

Lab products found in correlation

3 protocols using gt450 scanner

1

Contamination Augmentation for Pathology Images

Check if the same lab product or an alternative is used in the 5 most similar protocols
Contaminant slides were one each of low grade urothelial bladder tumor retrieved by transurethral resection (“bladder”), post-delivery non-adherent blood received with a placenta (“blood”), colonic mucosal biopsy showing a tubular adenoma (“colon”), fallopian tube fimbriae removed by salpingectomy for fertility control (“fallopian”), full thickness section of placental disc (“placenta”), hypertrophic prostate excised by holmium laser (“prostate”), excised skin with intradermal nevus (“skin”), small bowel resected after traumatic injury (“small bowel”), and umbilical cord cross sections (“umbilical”). Contaminant slides were reviewed to ensure they themselves were not contaminated, but not otherwise selected or resampled. Contaminant slides were scanned using the same Leica GT450 scanner as placentas and prostate biopsies and underwent the same feature extraction with the same magnification, tile size, and overlap. To add contaminant to a patient slide, a random subset of contaminant patches was selected and appended to the set of feature vectors from the placenta. The quantity of contaminant was varied, with 10% of contaminant indicating 10 patches of contaminant added for every 100 patches of relevant tissue. Unlike typical image corruption paradigms, the patient tissue patches are not altered or removed.
Irmakci I., Nateghi R., Zhou R., Ross A.E., Yang X.J., Cooper L.A, & Goldstein J.A. (2023). Tissue contamination challenges the credibility of machine learning models in real world digital pathology. medRxiv.
+ Open protocol
+ Expand
2

Placental Digitization and Diagnosis

Cited 1 time
Check if the same lab product or an alternative is used in the 5 most similar protocols
Inclusion criteria were patients that underwent placental examination and reporting at our institution between 2011 and 2023 and had slides scanned as part of an ongoing digitization study (IRB: STU00214052). Each model used additional criteria as noted below. Placentas were examined and diagnoses rendered according to the Amsterdam criteria or precursor guidelines (27 (link)). Slides were digitized using a Leica GT450 scanner with a 40x objective magnification (0.263 microns per pixel). The 10x magnification layer was used for all studies. Pathology reports were obtained from the institutional electronic data warehouse (EDW) and processed using natural language processing (42 (link)). Patient and diagnosis information was stored in REDcap.
Irmakci I., Nateghi R., Zhou R., Ross A.E., Yang X.J., Cooper L.A, & Goldstein J.A. (2023). Tissue contamination challenges the credibility of machine learning models in real world digital pathology. medRxiv.
+ Open protocol
+ Expand
3

Placental Pathology Database for Research

Cited 1 time
Check if the same lab product or an alternative is used in the 5 most similar protocols
We identified patients that underwent delivery and placental examination at our institution between 2011 and 2022 and had slides available for scanning. Our scanning program is ongoing and the material scanned represents a mixture of prospectively scanned slides and targeted archival retrieval, e.g. for MPVFD cases. Slides were digitized on a Leica GT450 scanner with a 40x objective magnification (0.263 microns per pixel). Corresponding placental pathology reports were extracted from the institutional electronic data warehouse (EDW). Natural language processing was used to parse reports.[41 (link)] Placentas with retroplacental hemorrhage / hematoma or chorangioma or with multiple classes of lesions were excluded. Placentas were classified as having infarct, IVT, PVFD, or none of these (normal). PVFD cases included MPVFD and focal PVFD, which was defined as a macroscopic placental lesion characterized by encasement of villi in perivillous fibrin. Both lesions are distinguished from the normal degree of perivillous fibrin by the formation of macroscopic lesions.[42 (link)]
Patient and diagnosis data was stored in REDcap.[43 (link)] Slide images were reviewed by a perinatal pathologist aware of the original diagnosis using Digital Slide Archive (DSA).[44 (link)] The study was approved by our local institutional review board (STU00214052).
Goldstein J.A., Nateghi R., Irmakci I, & Cooper L.A. (2023). Machine learning classification of placental villous infarction, perivillous fibrin deposition, and intervillous thrombus. Placenta, 135, 43-50.
+ Open protocol
+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required

Sign up now

Revolutionizing how scientists
search and build protocols!