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Three-dimensional lesion segmentation is required for analysis of radiomic features and lesion growth kinetics. In clinical trials, radiologists apply the Response Evaluation Criteria in Solid Tumors (RECIST), by manually annotating the long and short diameters of a lesion on a single 2D axial slice (RECIST slice), where the lesion looks largest. We developed a novel approach that leverages the RECIST annotations to segment lesions in 3D on CT scans. We start with bounding box and center point prompts derived from RECIST long and short diameters on RECIST slice. Iteratively, we perform prompted segmentation using Segment Anything Model (SAM) on off-RECIST slices towards the superior and inferior direction until all slices are segmented. To optimize the performance of SAM, we fine-tuned the mask decoder. In addition, it is crucial to detect where the lesions disappear at the superior and inferior direction to prevent over segmentation. We developed a multi-task framework for lesion existence classification and segmentation and further compared the parallel framework and cascaded framework. We used an internal dataset consisting of 2053 and 200 3D lesions for fine-tuning of SAM decoder and testing, respectively. Baseline SAM, SAM with fine tuning, SAM with parallel multi-task fine tuning, and SAM with cascaded multitask fine tuning have Dice scores of 0.4745±0.2138, 0.7136±0.1277, 0.6985±0.1312, and 0.7239±0.1321, respectively. Our experiments proves that multi-task learning is an effective way for 3D segmentation with SAM, and cascaded framework performs better than parallel framework.
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