Lite ENSAM: a lightweight cancer segmentation model for 3D Computed Tomography

TL;DR

Lite ENSAM is a lightweight deep learning model designed for efficient volumetric tumor segmentation in CT scans, aiming to improve cancer treatment assessment through automated analysis.

Contribution

It introduces a novel lightweight adaptation of ENSAM architecture tailored for volumetric tumor segmentation from RECIST-annotated CT scans.

Findings

Achieved a Dice Similarity Coefficient of 60.7%

Normalized Surface Dice of 63.6% on test set

Inference time of 14.4 seconds on CPU

Abstract

Accurate tumor size measurement is a cornerstone of evaluating cancer treatment response. The most widely adopted standard for this purpose is the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1, which relies on measuring the longest tumor diameter in a single plane. However, volumetric measurements have been shown to provide a more reliable assessment of treatment effect. Their clinical adoption has been limited, though, due to the labor-intensive nature of manual volumetric annotation. In this paper, we present Lite ENSAM, a lightweight adaptation of the ENSAM architecture designed for efficient volumetric tumor segmentation from CT scans annotated with RECIST annotations. Lite ENSAM was submitted to the MICCAI FLARE 2025 Task 1: Pan-cancer Segmentation in CT Scans, Subtask 2, where it achieved a Dice Similarity Coefficient (DSC) of 60.7% and a Normalized Surface Dice (NSD) of 63.6% on the hidden test set, and an average total RAM time of 50.6 GBs and an average inference time of 14.4 s on CPU on the public validation dataset.