Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT

Erickson, Peter D.; Mathai, Tejas Sudharshan; Summers, Ronald M.

doi:10.1007/978-3-031-16760-7_17

Cited by 3 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, radiologists do not annotate all findings in all slices as the manual measurement is time-consuming and cumbersome. There have been many approaches in literature [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] tackle the problem of bone lesion detection and segmentation. But, a vast majority are for specific lesion types (e.g.…”

Section: Introductionmentioning

confidence: 99%

Weakly-supervised detection of bone lesions in CT

Sheng,

Mathai,

Shieh

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

View full text Add to dashboard Cite

The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. CT is routinely used to measure the size of lesions in the bones. However, they can be difficult to spot due to the wide variations in their sizes, shapes, and appearances. Precise localization of such lesions would enable reliable tracking of interval changes (growth, shrinkage, or unchanged status). To that end, an automated technique to detect bone lesions is highly desirable. In this pilot work, we developed a pipeline to detect bone lesions (lytic, blastic, and mixed) in CT volumes via a proxy segmentation task. First, we used the bone lesions that were prospectively marked by radiologists in a few 2D slices of CT volumes and converted them into weak 3D segmentation masks. Then, we trained a 3D full-resolution nnUNet model using these weak 3D annotations to segment the lesions and thereby detected them. Our automated method detected bone lesions in CT with a precision of 96.7% and recall of 47.3% despite the use of incomplete and partial training data. To the best of our knowledge, we are the first to attempt the direct detection of bone lesions in CT via a proxy segmentation task.

show abstract

Section: Introductionmentioning

confidence: 99%

Weakly-supervised detection of bone lesions in CT

Sheng,

Mathai,

Shieh

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

View full text Add to dashboard Cite

show abstract

“…[4][5][6] As the 3D lesion extent can be a potential indicator of response to treatment, it is crucial to detect and label the 3D lesions, such that their size can be accurately measured based on current guidelines. Prior approaches [3][4][5][7][8][9][10][11][12][13][14] for 3D lesion detection used the publicly available DeepLesion dataset, 7 but it contains incomplete annotations 5,6,15 and severe class imbalances. 6,15 In this work, we designed a self-training pipeline for 3D lesion detection and tagging.…”

Section: Introductionmentioning

confidence: 99%

“…Prior approaches [3][4][5][7][8][9][10][11][12][13][14] for 3D lesion detection used the publicly available DeepLesion dataset, 7 but it contains incomplete annotations 5,6,15 and severe class imbalances. 6,15 In this work, we designed a self-training pipeline for 3D lesion detection and tagging. We used a limited 30% data subset of DeepLesion consisting of lesion bounding boxes and coarse body part labels to train a VFNet model 16 for 2D lesion detection and tagging.…”

Section: Introductionmentioning

confidence: 99%

3D universal lesion detection and tagging in CT with self-training

Frazier¹,

Mathai²,

Liu³

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

View full text Add to dashboard Cite

Radiologists routinely perform the tedious task of lesion localization, classification, and size measurement in computed tomography (CT) studies. Universal lesion detection and tagging (ULDT) can simultaneously help alleviate the cumbersome nature of lesion measurement and enable tumor burden assessment. Previous ULDT approaches utilize the publicly available DeepLesion dataset, however it does not provide the full volumetric (3D) extent of lesions and also displays a severe class imbalance. In this work, we propose a self-training pipeline to detect 3D lesions and tag them according to the body part they occur in. We used a significantly limited 30% subset of DeepLesion to train a VFNet model for 2D lesion detection and tagging. Next, the 2D lesion context was expanded into 3D, and the mined 3D lesion proposals were integrated back into the baseline training data in order to retrain the model over multiple rounds. Through the self-training procedure, our VFNet model learned from its own predictions, detected lesions in 3D, and tagged them. Our results indicated that our VFNet model achieved an average sensitivity of 46.9% at [0.125:8] false positives (FP) with a limited 30% data subset in comparison to the 46.8% of an existing approach that used the entire DeepLesion dataset. To our knowledge, we are the first to jointly detect lesions in 3D and tag them according to the body part label.

show abstract

“…ULD with tagging (ULDT) adds diagnostic value by tagging the lesion with the body part in which the lesion is located. Prior work on ULDT [13][14][15][16] used the publicly available DeepLesion dataset, 2 but it is incomplete as only clinically significant lesions are annotated while others remain unannotated. It is also severely class-imbalanced 15 with considerable over-representation of certain classes (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Prior work on ULDT [13][14][15][16] used the publicly available DeepLesion dataset, 2 but it is incomplete as only clinically significant lesions are annotated while others remain unannotated. It is also severely class-imbalanced 15 with considerable over-representation of certain classes (e.g. liver, lung) over other under-represented classes (e.g.…”

Section: Introductionmentioning

confidence: 99%

Correcting class imbalances with self-training for improved universal lesion detection and tagging

Shieh¹,

Mathai²,

Liu³

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

View full text Add to dashboard Cite

Universal lesion detection and tagging (ULDT) in CT studies is critical for tumor burden assessment and tracking the progression of lesion status (growth/shrinkage) over time. However, a lack of fully annotated data hinders the development of effective ULDT approaches. Prior work used the DeepLesion dataset (4,427 patients, 10,594 studies, 32,120 CT slices, 32,735 lesions, 8 body part labels) for algorithmic development, but this dataset is not completely annotated and contains class imbalances. To address these issues, in this work, we developed a self-training pipeline for ULDT. A VFNet model was trained on a limited 11.5% subset of DeepLesion (bounding boxes + tags) to detect and classify lesions in CT studies. Then, it identified and incorporated novel lesion candidates from a larger unseen data subset into its training set, and self-trained itself over multiple rounds. Multiple self-training experiments were conducted with different threshold policies to select predicted lesions with higher quality and cover the class imbalances. We discovered that direct self-training improved the sensitivities of over-represented lesion classes at the expense of under-represented classes. However, upsampling the lesions mined during self-training along with a variable threshold policy yielded a 6.5% increase in sensitivity at 4 FP in contrast to self-training without class balancing (72% vs 78.5%) and a 11.7% increase compared to the same self-training policy without upsampling (66.8% vs 78.5%). Furthermore, we show that our results either improved or maintained the sensitivity at 4FP for all 8 lesion classes.

show abstract

Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT

Cited by 3 publications

References 20 publications

Weakly-supervised detection of bone lesions in CT

Weakly-supervised detection of bone lesions in CT

3D universal lesion detection and tagging in CT with self-training

Correcting class imbalances with self-training for improved universal lesion detection and tagging

Contact Info

Product

Resources

About