SEG: Segmentation Evaluation in absence of Ground truth labels.

ABSTRACT: Identifying individual cells or nuclei is often the first step in the analysis of multiplex tissue imaging (MTI) data. Recent efforts to produce plug-and-play, end-to-end MTI analysis tools such as MCMICRO ¹ - though groundbreaking in their usability and extensibility - are often unable to provide users guidance regarding the most appropriate models for their segmentation task among an endless proliferation of novel segmentation methods. Unfortunately, evaluating segmentation results on a user's dataset without ground truth labels is either purely subjective or eventually amounts to the task of performing the original, time-intensive annotation. As a consequence, researchers rely on models pre-trained on other large datasets for their unique tasks. Here, we propose a methodologi

SUBMITTER: Sims Z 

PROVIDER: S-EPMC9980141 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature